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Co-Sponsored Event

Texas Criminal Defense Lawyers Association Harris County Criminal Lawyers Association Montgomery County Criminal Defense Lawyers Association

Course Directors: Sean Hightower, Laurie Key, & Brent Mayr September 3-4, 2026 • The Woodlands Resort

P: 512.478.2514 • F: 512.469.9107 • www.tcdla.com • 6808 Hill Meadow Dr., Austin, TX 78736

AI & ANATOMY OF A TRIAL SEMINAR INFORMATION Date Location Course Director Total CLE Hours

September 3-4, 2026 The Woodlands Resort, 2301 N. Millbend Dr, The Woodlands, TX 77380 Sean Hightower, Laurie Key, & Brent Mayr 13.00 Ethics: 1.00

Thursday, September 3, 2026 Time 7:45 am

CLE

8:15 am 8:30 am

1.00

9:30 am 9:40 am

1.00

Opening Remarks

Course Directors

Law Office Management Using AI

Sean Hightower

Effective Storytelling: Using AI to Craft Powerful Openings & Closings

Rocky Ramirez

Break 1.00

12:00 pm 12:40 pm

Speaker

JusticeText Demo

10:40 am 11:00 am

Topic Registration & Continental Breakfast

Daily CLE Hours: 7.00 Ethics: 0.00

Pre-Trial Investigation: Leveraging AI to Set the Tone & Tempo Before Trial Begins

Cody Breunig

Lunch 1.00

1:40 pm

Voir Dire Strategy: Using AI to Identify Bias & Shape Jury Selection

Jason Bloom

Break

2:00 pm

1.00

AI Legal Research

Kyle Therrian

3:00 pm

1.00

Developing Your Case Theory Using AI

Grant Scheiner

4:00 pm 4:20 pm 5:20 pm

Break 1.00

How to Present Visual Evidence

Clifford Duke

Adjourn

TCDLA :: 6808 Hill Meadow Dr :: Austin, Texas :: 512.478.2514 p :: 512.469.9107 f :: www.tcdla.com

AI & ANATOMY OF A TRIAL SEMINAR INFORMATION Date Location Course Director Total CLE Hours

September 3-4, 2026 The Woodlands Resort, 2301 N. Millbend Dr, The Woodlands, TX 77380 Sean Hightower, Laurie Key, & Brent Mayr 13.00 Ethics: 1.00

Friday, September 4, 2026 Time 7:45 am

CLE

8:15 am

Topic Registration & Continental Breakfast

Daily CLE Hours: 6.00 Ethics: 1.00 Speaker

Opening Remarks

Course Directors

8:30 am

1.00

Hands on How to Use AI Platforms

Don Flanary

9:30 am

1.00

Cross-Examination: Using AI to Dissect the Adversarial Witness

Neal Davis

10:30 am 10:50 am

Break 1.00

11:50 am 12:30 pm

The Expert Witness: Using AI to Prepare, Challenge, & Litigate Rule 705 Experts

Eric Devlin

Lunch 1.00

1:30 pm

How to Tell if It is Fake – Videos, Pictures, or AI Research?

Blakely Mohr

Break

1:50 pm

1.00 Ethics

Ethics of AI

Brent Mayr

2:50 pm

1.00

Attacking the State Usage & How Fight Cops or DA Using AI

Chris Fredericks

3:50 pm

Adjourn

TCDLA :: 6808 Hill Meadow Dr :: Austin, Texas :: 512.478.2514 p :: 512.469.9107 f :: www.tcdla.com

Texas Criminal Defense Lawyers Association

AI & Anatomy Of A Trial Table of Contents Thursday, September 3, 2026 speakers Sean Hightower Rocky Ramirez Cody Breunig Jason Bloom Kyle Therrian Grant Scheiner Clifford Duke

topic Law Office Management Using AI Effective Storytelling: Using AI Pre-Trial: Investigation The Use of Artificial Intelligence in the Jury AI Legal Research Developing Your Case Theory Using AI Putting Pictures on The Page

Friday, September 4, 2026 speakers Don Flanary Neal Davis Eric Devlin Blakely Mohr Brent Mayr Chris Fredericks

topic Hands On How to Use AI Platforms Cross Examination: Using AI AI with Experts How to Tell if It is Fake Ethics Of AI Defending Against The Machine

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: Law Office Management Using AI

Speaker:

Sean Hightower

310 North Mound St. Nacogdoches, TX 75961-5539 936.560.3300 phone seanhightower@yahoo.com email website: http://www.thegoodlawyer.com/

Law Office Management Using AI

Texas Criminal Defense Lawyers Association

Law Office Management Using AI

AI Tools for Lawyers – A Practical Guide Artificial intelligence (“AI”) is transforming the legal industry at a remarkable pace and now plays a practical, everyday role in law firms across the globe. Whether automating routine tasks, conducting advanced legal research, or enhancing client services, AI has the potential to reshape how legal professionals operate. However, with numerous AI platforms and tools available, selecting the right solution for your law firm can feel overwhelming. This guide aims to simplify that decision and provide a brief overview of popular GenAI tools used by attorneys.

Benefits and Uses We will begin with looking at some of the benefits of AI in your practice: •

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Increased Efficiency: AI tools can significantly enhance the efficiency of legal practices by automating routine tasks and expediting otherwise complex processes. For example, AI-powered document review systems can analyze vast volumes of information in a fraction of the time it would take a human, freeing up time to focus on other tasks.

Improved Accuracy: The precision of AI tools in handling data and performing analyses is unparalleled. AI systems can meticulously organize and manage critical and voluminous documents (such a medical records or voluminous expert reports).

Cost Savings: By automating labor-intensive tasks, AI tools contribute to significant cost savings for law firms. The reduction in "man hours" required for tasks like document review and data analysis allows firms to allocate resources more effectively.

Enhanced Research Capabilities: AI-powered research tools, such as Westlaw Co-Counsel and Filevine, provide attorneys with advanced capabilities to conduct comprehensive legal research. These tools can quickly sift through extensive legal databases to identify relevant case law, statutes, and legal precedents, empowering attorneys with the information needed to build compelling arguments. Additionally, AI systems can assist in generating helpful information for aspects of cases such as demand letters, which are crucial in settlement negotiations.

Better Client Service: AI tools enhance client service by improving communication and responsiveness. For example, AI systems can track communications, ensuring that follow-ups and action items are noted for future tasks. This proactive approach to case management can simplify tasks and improve client relationships.

Practical Applications: AI tools can be used to assist in the performance of tasks common to most law practices. For instance, AI-powered systems can 1

The specific areas where AI appears to be making the largest impact in the legal field: • Legal Research: AI can swiftly scan extensive case law databases, statutes, and regulations. These tools can pinpoint the most relevant results based on context, thereby saving hours of manual effort. Advanced AI can even identify patterns and suggest related issues that a manual search might miss.

• Document Review: Discovery is among the most time-consuming aspects of litigation. AI tools can efficiently classify, tag, and extract key information from thousands of documents. Predictive coding and Technology Assisted Review (TAR) alleviate the burden on attorneys while enhancing accuracy. Some platforms also offer sentiment analysis and redaction automation.

• Contract Review and Analysis: AI platforms can analyze contracts to highlight missing clauses, pinpoint risks, and ensure regulatory compliance. These tools assist attorneys in reducing review time from hours to minutes and support things like version comparisons and clause libraries.

• Litigation Prediction: AI tools leverage data analytics to predict litigation outcomes, judge behaviors, and opposing counsel strategies. This enhances strategy development and client counseling by enabling firms to tailor arguments, set settlement expectations, and determine venue decisions based on datadriven insights.

• Client Communication and Intake: Chatbots and virtual assistants can handle client inquiries, schedule appointments, and collect initial information. Several tools are specifically designed for legal applications and can integrate with systems to enhance client onboarding and ensure consistent engagement.

• Billing and Time Tracking: AI can automate time entry, suggest billing codes, and identify billing inefficiencies. Several tools offer AI-driven timekeeping features that automatically track time spent on calls, emails, and document editing, ensuring more accurate and complete billing.

Strategy for Use of AI A successful AI adoption strategy starts with clearly understanding your firm's unique needs. Some important factors you may consider: •

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Practice Area: Different legal practices require different AI functionalities. For example, personal injury attorneys may focus on client intake and case management, while intellectual property firms may prioritize document search and pattern recognition for prior art. Employment law firms might benefit from compliance monitoring and real estate attorneys from automated deed and title reviews.

Firm Size and Structure: A solo attorney or small firm may prefer cloud-based, low-cost solutions that require minimal IT support, while a large firm with an IT department might invest in a robust on-premises AI platform with advanced 2

Law Office Management Using AI

assist in scheduling and managing deadlines, reducing the risk of missed deadlines, and ensuring compliance with court requirements.

Law Office Management Using AI

•

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analytics, user permissions management, and multi-office integration.

Case Volume and Complexity: High-volume firms will benefit more from automation and document analysis tools that reduce manual labor and human error. Complex litigation firms might prioritize advanced legal analytics and collaborative features to support multi-lawyer teams working on high-stakes cases.

Client Expectations: Are your clients demanding faster turnarounds or more detailed analytics? Tech-savvy clients may expect online portals, real-time updates, and transparency in billing, all of which AI-driven solutions can support or enhance.

Budget Constraints: Determine how much your firm can invest in financial resources and personnel training. Account for the upfront costs of licenses or subscriptions and long-term expenses related to maintenance, upgrades, and user training.

When comparing AI platforms, keep an eye on these key capabilities: • •

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Customizability: Your platform should adapt to your firm's workflows, not vice versa.

Data Security and Compliance: Ensure the platform meets strict data protection standards. Features like role-based access, audit trails, and encryption should be standard.

User Experience: Choose platforms with an intuitive interface and clear user documentation. Consider platforms that provide visual workflows, searchable help centers, and embedded support tools to reduce training time.

Scalability: Ensure the platform can grow as your firm expands. It should be able to accommodate more users, larger data sets, and additional functionalities without a performance drop.

Additionally, key evaluation criteria should include: •

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Industry Experience: Does the vendor specialize in legal tech? Have they worked with firms like yours? Look for vendors who demonstrate domain-specific knowledge and an understanding of legal workflows.

Client Reviews and References: Look for third-party reviews, testimonials, and case studies that address performance and customer satisfaction. Peer feedback is often the most reliable predictor of vendor success.

Customer Support: Evaluate training, onboarding resources, and technical support availability. Live chat, helpdesk tickets, and dedicated account managers can make a big difference during implementation and troubleshooting.

Contract Terms: Avoid long-term lock-ins, automatic renewals, or unclear pricing structures. Flexible, short-term contracts are often preferable during initial deployment.

Trial Period: Insist on a pilot program. Run the platform through real-world scenarios to evaluate ease of use, output accuracy, and compatibility with firm processes.

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Virtually every tool or platform listed offers not only demonstrations, but free trials. This offers practitioners the ability to evaluate each of the tools for themselves without committing to a long-term obligation. We provide an overview of options below to get you started depending on your needs:

1. Case Management Tools •

2nd Chair.ai - 2nd Chair allows users to upload all its files to a system that can analyze the documents, prepare summaries, and accelerate the identification of facts within those documents. 2nd Chair also provides legal research help and allows users to see the sources of its answers.

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Clio Duo – Clio’s cloud-based legal practice management platform centralizes law firm operations and offers GenAI add-ons. Clio Duo embeds within Clio Manage, allowing users to access GenAI and machine learning within their Clio database. Powered by Microsoft Azure OpenAI GPT-4, Clio Duo summarizes documents, drafting e-mails and letters, and helps prioritize workflows.

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Filevine - Filevine automates financial data extraction, labeling, and categorization. It tracks billable hours and invoices, and analyzes lead data from online forms, social media, and referrals. Filevine’s ImmigrationAI feature lets attorneys quickly identify relevant forms, autofill them with client data, and provide translations in over 170 languages. Filevine’s Demands AI creates documents such as custom demand letters by analyzing case details and relevant legal precedents. Filevine’s Sidebar AI uses a virtual AI legal assistant to provide real-time support.

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LEAP - LEAP tracks and records billable hours. LEAP’s time analytics can help identify areas to optimize time management. LEAP’s AI assistant, LawY, addresses legal questions by analyzing case law. LEAP’s Document Generator (LawY) uses historical cases and built-in templates to automate document creation including correspondence and templates.

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MyCase - MyCase automates document generation, email drafting, and data extraction. It also analyzes context and legal precedents to create affidavits, contracts, and other documents. MyCase’s GenAI-enhanced search helps locate and retrieve important case information. MyCase also seeks to improve workflow by automatically creating tasks and events based on information extracted from uploaded documents.

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Smokeball - Smokeball automates billable hours tracking to generate detailed time entries. If given access, Smokeball can scan e-mail inboxes to save emails and attachments to their related cases. Smokeball also provides detailed

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Law Office Management Using AI

AI Tools

Law Office Management Using AI

financial analytics for particular clients and matters. Smokeball can also help automatize intake processes and legal form creation.

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CosmoLex - CosmoLex is cloud-based law practice management software that integrates trust and business accounting, time tracking, billing, email & document management, and tasks & calendaring, in a single application. CosmoLex offers features like an interactive GenAI chatbot for real-time case questions, automated document drafting, and searchable medical chronologies and case timelines. However, CosmoLex is primarily a billing and accounting platform.

2. Case Outcome Prediction Tool •

Lex Machina - Lex Machina uses AI technology to provide case predictions. Lex Machina, offered by LexisNexis, pitches itself as providing predictions regarding the outcome of cases based on the history of the court, judge(s), lawyers, and parties involved in the matter. Lex Machina identifies the prior cases it relies on and provides visualizations to help attorneys more easily understand the information presented.

3. Contract Analysis and Management Tools •

Diligen - Diligen is a machine learning contract analysis tool. Diligen can match due diligence documents with contractual provisions, find specific provisions across agreements and provide contract summaries. Diligen enables automated contract drafting and clause identification. Diligen is compatible with Clio and can run its analyses on documents in a Clio system.

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Ironclad - Ironclad is a contract management system. Ironclad allows companies to manage their contracts throughout the entire contract cycle from drafting to expiration. Ironclad uses AI to data tag contracts, suggest redlines, flag unallowed clauses, and create a dashboard contract reporting. Ironclad offers a GenAI assistant, Jurist, capable of drafting contracts, contract clauses, redlines, and performing research on your contract database as well as legal and regulatory resources. Ironclad also offers a prompt library to help users new to GenAI to quickly become proficient.

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Kira Systems - Kira Systems is a machine learning contract analysis tool. Kira Systems, offered by Litera, uses machine learning to identify, extract, and analyze contractual clauses. Kira Systems can also compare data from documents with contract provisions. Kira Systems’ marketing focuses largely on analyzing contracts as part of the due diligence process.

4. E-Discovery Tools •

Everlaw - Everlaw is an e-discovery platform that integrates AI into its research and case preparation platform to identify documents through analytics and counterarguments. Everlaw’s Storybuilder uses machine learning and GenAI to 5

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Relativity - Relativity uses generative AI for efficient document review during the research process. Relativity uses machine learning to narrow the scope of documents that need human review before production. It also can help identify the most important documents that have been produced by opposing counsel.

Relativity is also a leader in providing analytics and visualizations of discovery documents.

5. Legal Research Tools •

Bloomberg Law – Bloomberg Law uses GenAI and machine learning to provide immediate results for queries against both case documents and legal sources.

Using machine learning, Bloomberg’s Smart Code pulls case extracts interpreting both federal and state law. Among its features, it ranks each case discussion as strong, moderate, or weak to streamline research. Bloomberg also offers a Brief Analyzer which automatically checks citations, searches cited case law, and can suggest related authorities.

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Harvey AI - Harvey AI is a GenAI-powered legal research tool that operates as a chatbot. HarveyAI is built on OpenAI technology. However, Harvey AI also uses a separate legal database to improve its research answers. and a law firm’s own work products and templates to specifically support legal work. Harvey AI also allows users and firms to upload historical case documents which its service will scan as part of its legal research and document analysis. Harvey AI is designed to assist firms with contract analysis, due diligence, litigation, and regulatory compliance.

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Lexis+ - Lexis+ uses GenAI to search its databases, improving its search functionality. Lexis+ also offers a powerful document analysis tool that allows users to upload briefs which are then analyzed for additional authority supporting the arguments raised or authority that contradicts or may weaken those arguments. Lexis+ also offers litigation analytics, showing prior results from your particular jurisdiction and judge.

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vLex fastcase - Vincent AI’s vLex uses GenAI search against a database containing case law, legislative information, law journals, and other secondary sources. vLex will also summarize documents, including legal documents such as contracts. For litigators, vLex uses GenAI to build timelines and suggest arguments. vLex is powered by legal sources from all fifty states and seventeen countries.

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Westlaw Edge - Westlaw Edge uses GenAI trained on Westlaw’s KeyCites to help ensure attorneys collect the accurate sources during the research process.

Westlaw Edge analyzes briefs generating a report indicating authority that may 6

Law Office Management Using AI

develop timelines and organize the story of the parties’ dispute. Everlaw can also summarize key documents and create draft communications with verifiable citations to documents.

Law Office Management Using AI

have been missed in research or which contradicts the authority cited by opposing counsel.

6. Legal Specific LLMs •

CoCounsel – CoCounsel is a large language model powered by OpenAI. This product is made available by Westlaw/Thomson Reuters but is compatible with other legal service software platforms as well. This AI legal assistant tool is knowledgeable, reliable, and secure enough to support the practice of law.

Cocounsel operates similarly to other LLMs but it was trained for the law.

Importantly, while Cocounsel is powered by OpenAI, it has a dedicated server that does not (currently) allow OpenAI to train on the uploaded data.

7. Virtual Receptionists •

Smith.ai - Smith.ai is a virtual receptionist. While AI technology is not able to replace attorneys, it can replace or limit the burden on existing staff. Although the “virtual receptionist” is an actual human, Smith.ai uses AI with its chatbot features to decide how to log and route calls. Both the receptionist and chat features integrate with Clio.

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Texas Criminal Defense Lawyers Association

September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: Effective Storytelling: Using AI Speaker:

Rocky Ramirez

4022 Green Meadow #504 San Angelo, TX 76904 325.229.4654 phone rramirez@cvpdo.org email

Effective Storytelling Using AI

AI & Anatomy of a Trial

Claude Shannon’s Information Theory for Trial Lawyers Rocky Ramirez

Effective Storytelling Using AI

I. Information Theory In 1948, a Bell Labs engineer named Claude Shannon asked a question nobody had answered precisely: how do you measure information? His answer created an entire field and is the basis that modern day AI is built on. It’s what makes the internet work and what guides machine learning models and cryptocurrency. His insight was this; the information in a message is a function of how unexpected it is. A message you could have predicted in advance carries no information. A message that surprises you, carries a lot. Claude E. Shannon, A Mathematical Theory of Communication, 27 Bell Sys. Tech. J. 379 (1948).

A State's witness who takes the stand and says exactly what was in the offense report has told us nothing we didn’t already know. It’s the witness that deviates from the script – who adds the detail that wasn't in the report or contradicts the detail that was — they are the ones who can turn the case on its head. We instinctively call the first witness "cumulative”, and our ears perk up for the second. Shannon's contribution was to figure out how to mathematically measure that information.

The math isn’t too hard – even for a lawyer. If an event had probability p of occurring, observing it carries −log₂ *p bits of information. Information is measured in bits, and the way we measure it is by how much that information halves our expectations. A coin flip that can go either way is equal to one bit of information. An event you'd have given one-in-four odds of happening carries two bits of information.

But an event that was near-certain to happen carries zero bits — no matter how long the witness spent saying it or how many times they repeated it. We are measuring the surprise value of the information.

Each additional bit means the observation cut the space of what you believed in half again.

II. Entropy Entropy is Shannon’s second idea, and it is just expected surprise: how much uncertainty a situation holds before you learn anything more. If you know that a coin is rigged, then flipping that coin has low entropy.

You already know how its going to land. A fair coin has max entropy for two outcomes, so it equals one full bit.

Now put twelve people in a box. At the start of trial, the jury sits — in theory — at high entropy. Every piece of evidence either moves that distribution of the jury’s belief, or it doesn’t. A trial, seen through the eyes of Shannon, is the State's attempt to push the jury's entropy down to near zero. The defense's job is to show why the evidence cannot get them there.

That framing changes what "strong evidence” means. The strength of a piece of evidence is not measured by its volume, vividness, or admissibility. Strength is how much uncertainty the item removes or introduces about which story is true. The defense’s goal is to maximize Shannon entropy.

III. Measuring a case fact

For each item of discovery, ask, under each competing theory of the case: "If this theory were true, how likely would I be to see this piece of evidence?" From those likelihoods, the math (Bayes' rule plus Shannon's formulas) does the rest, and four results matter for trial work:

2. Relevance is not informativeness. Cell-site records placing the client's phone near the scene are perfectly relevant and survive every objection. But if the defense theories already concede presence, those records deliver their bits to an element nobody is fighting over. The State must introduce that evidence, but it has no informational value according to Shannon.

3. Questions of credibility add noise to the signal. An accomplice testifying because they got a deal can say something shocking, and it is still shocking to hear — it just shouldn't move anyone. Shannon would say the witness is a noisy channel between the event and the jury. As their credibility falls, the information that survives transmission collapses toward zero. Surprise and information come apart.

4. Redundancy. Three exhibits that each seem devastating — the DNA, the print, the eyewitness — may be three windows onto one underlying fact. Arguing all three is substantially arguing once. Information theory measures the overlap, which tells you what corroboration is worth and where a second witness adds weight versus merely adding minutes.

IV. Information in openings and closings The opening is the maximum-entropy moment. The jury's uncertainty will never be higher than before the first word of evidence, which means the first facts land at their full information value — this is primacy, stated in bits. Spend the opening on the highest-information facts of your story, not on chronology for its own sake.

The closing is a sequencing problem. Each fact's weight depends on what the jury has already absorbed, so order matters — and so does knowing when to stop. Ranked by marginal contribution to doubt, most files show the same shape: a handful of items carry the argument, and the rest add almost nothing. Not because they're weak, but because by the time you reach them the jury has already moved as far as those facts can move them. Spending closing time on those facts just dilute your strongest material. And the facts that cut against you should be answered directly — an information audit won't let you quietly rank them last hoping that the jury didn’t notice.

This is also where AI fits. A model can read the packet, extract the exhibits, and draft the worksheet — the clerical layer. The likelihood judgments stay blank, because deciding that a suggestive photo array is far more likely under misidentification than under guilt is the lawyering, and it cannot be delegated without anchoring your judgment to a machine's. And we should never do that. The tool does math on your judgment; it does not substitute for it.

V. Paulson v. State

Effective Storytelling Using AI

1. Weight in bits. An item that is equally likely under every theory of the case carries zero bits about which theory is true — however admissible, however dramatic, however long the sponsoring witness testified. In a typical discovery packet, a surprising fraction of the contested items turn out to have very little informational value.

Effective Storytelling Using AI

Reasonable doubt is not a number. Texas deliberately gives juries no definition of reasonable doubt at all.

Paulson v. State, 28 S.W.3d 570 (Tex. Crim. App. 2000). Everything above is a preparation aid — a way to decide what to argue and in what order. Putting a percentage in front of a jury is an excellent way to manufacture reversible error out of a case you were winning.

Information value of evidence should not be used as an optimal path to finding your closing. It’s just a strong heuristic. Evidence can be synergistic — two items that are modest alone can be devastating together — so no ranking procedure is guaranteed to find the single best sequence. Trust the shape it reveals; keep your judgment in the loop on the final order you choose to present them in. Case facts are not independent. Five exhibits that all say "different man" are five windows onto one fact and multiplying them as if independent might produce confident nonsense.

VI. The point None of this replaces judgment; every number in the analysis is one the lawyer supplied. What the math adds is that the judgment becomes explicit and auditable. When a co-counsel disagrees with your closing, the disagreement becomes "your 70% on the photo array is too high" instead of dueling intuitions that can’t be measured. Shannon gave us a ruler for surprise. The measuring is still ours.

Texas Criminal Defense Lawyers Association

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Pre-Trial: Investigation

Speaker:

Cody Breunig

504 Spring Hill Drive, Suite 360 Spring, TX 77386

346.459.7991 phone cody@bdforensics.com email https://www.bdforensics.com/ website

Pre-Trial Investigations

Topic:

PRE-TRIAL INVESTIGATION: LEVERAGING AI TO SET THE TONE AND TEMPO BEFORE TRIAL BEGINS

Pre-Trial Investigations

CODY BREUNIG CrossLogic Intelligence, LLC & Black Dog Forensics LLC Spring, Texas

Texas Criminal Defense Lawyers Association AI & ANATOMY OF A TRIAL September 3 to 4, 2026 The Woodlands, Texas

Table of Contents I.

INTRODUCTION ............................................................................................................................................................................ 4

II.

WHY SPEED IS THE WHOLE GAME......................................................................................................................................... 4 A.

Time Is the Battlefield ................................................................................................................................................................. 4

B.

The Pace of the Tools .................................................................................................................................................................. 5

C.

What Tone and Tempo Actually Mean ..................................................................................................................................... 5

III.

BUILDING YOUR AI TOOLKIT ............................................................................................................................................. 5

A.

Four Kinds of Tools (Addressed Here) ...................................................................................................................................... 5

B.

Choosing the Right Tool for the Job .......................................................................................................................................... 6

C.

Cost, Setup, and the Hardware That Helps............................................................................................................................... 6

D.

Security Before Speed ................................................................................................................................................................. 6

IV.

RAPID DISCOVERY AND RECORDS REVIEW .................................................................................................................. 6 Triage First: Build a Map Before You Read ............................................................................................................................. 7

B.

Summarize, Then Verify ............................................................................................................................................................. 7

C.

Timelines and Cross-Referencing .............................................................................................................................................. 7

D.

Body-Worn Camera and Audio at Scale ................................................................................................................................... 8

E.

Phone Extractions and Digital Records ..................................................................................................................................... 8

F.

Financial, Medical, and Business Records................................................................................................................................. 9

G.

Maximize Every Minute of the Day ........................................................................................................................................... 9

H.

Know What Not to Upload ......................................................................................................................................................... 9

V.

INDEPENDENT DEFENSE INVESTIGATION ........................................................................................................................... 9 A.

Capturing Witness Statements and Interviews ......................................................................................................................... 9

B.

Building the Witness Matrix......................................................................................................................................................10

C.

Developing Leads and Questions...............................................................................................................................................10

D.

Mapping the Scene and the Sequence .......................................................................................................................................10

E.

Surfacing Inconsistencies ...........................................................................................................................................................10

VI.

SURFACING THE EXCULPATORY: BRADY AND DISCOVERY LEVERAGE............................................................10

A.

The Duty and the Texas Framework ........................................................................................................................................11

B.

Reading the State's File Critically .............................................................................................................................................11

C.

Framing Targeted, Specific Demands.......................................................................................................................................11

D.

Tracking Productions and Gaps ...............................................................................................................................................11

VII.

PRE-TRIAL MOTIONS THAT SET THE TONE..................................................................................................................11

A.

From Findings to Motions .........................................................................................................................................................11

B.

Suppression and Preservation ...................................................................................................................................................12

C.

Motions in Limine and Beyond .................................................................................................................................................12

D.

AI as a Drafting Accelerator, With a Hard Rule .....................................................................................................................12

E.

The Tempo Effect .......................................................................................................................................................................13

VIII.

NEGOTIATION AND PLEA LEVERAGE.............................................................................................................................13

A.

Negotiating From a Documented Record .................................................................................................................................13

B.

Preparing Mitigation With AI ...................................................................................................................................................13

IX.

GUARDRAILS: ETHICS, VERIFICATION, AND ADMISSIBILITY ................................................................................13

A.

Competence and the Duty to Understand the Tools ................................................................................................................13

B.

Verify Everything .......................................................................................................................................................................14

C.

Confidentiality and Privilege .....................................................................................................................................................14

D.

Authentication and Reliability ..................................................................................................................................................14

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Pre-Trial Investigations

A.

E. X.

Candor, Disclosure, and Standing Orders................................................................................................................................14 A PRACTICAL PLAYBOOK ........................................................................................................................................................14

A.

Prompts That Earn Their Keep ................................................................................................................................................15

B.

A First-Week Adoption Plan .....................................................................................................................................................15

C.

Answering the Common Objections .........................................................................................................................................15

XI.

THE KEY TAKEAWAY ...........................................................................................................................................................15 AI AND TECHNOLOGY TOOL CHEAT SHEET ...........................................................................................17

Pre-Trial Investigations

APPENDIX A:

3

A note before we begin. This paper reflects my own work and is offered for educational purposes. It is not legal advice, and the tools and features described change quickly. Confirm the current capabilities and data practices of any tool before you rely on it in a case.

I. INTRODUCTION I am an investigator who specializes in digital forensics, and I am the founder of a web-based artificial intelligence platform built for criminal defense. I use AI every day, and I design it every day. That is the lens I bring to this paper. I am here as someone who lives in the same case files you do, and who has spent years watching where time goes in a case and where it is wasted.

II. WHY SPEED IS THE WHOLE GAME Before turning to the tools, it is worth being precise about why speed matters so much in the pre-trial phase, and why a marginal improvement in how fast you work translates into a real advantage for the client. The claim is not that faster is always better in some abstract sense.

The claim is that in criminal defense the calendar is an adversary, and the party that manages time well holds leverage the other side does not.

This paper is not a technical manual, and it is not a survey of everything artificial intelligence can do. It is meant to do one thing. It is meant to discuss the framework of how to use AI to get more out of the time you already have. In pre-trial investigation, time is the scarce resource. The record is large, the deadlines are fixed, and the client is presumed innocent while the clock runs against him. The side that often moves first, and moves fast, can set the tone and the tempo of the case.

This paper proceeds from a single thesis. In pre-trial investigation, the greatest value of artificial intelligence is speed. It multiplies what a solo or small defense team can accomplish with the time it has, and the side that investigates faster controls the tone and tempo of the case before trial ever begins. Everything that follows is organized around a simple test. If a tool does not save you time or sharpen your work, it is not worth the login.

Speed is how the defense closes the gap. Less hours you have to spend doing routine work, the more valuable you become. The faster you can absorb the discovery, build your own picture of the facts, and identify the weak points in the State's case, the sooner you can act on them, while witnesses still remember, while video still exists, and while the prosecutor is still forming a view of the file. A defense that reaches the same conclusions two months later reaches them after the window to use them has narrowed. The value of an insight in a criminal case is not fixed. It decays with time, and speed is how you capture that value before it fades.

A word about how to read what follows. You do not need to adopt every tool named here, and you should not try to do so all at once. Pick one part of your workflow that consistently eats your time, for me the first pass through discovery is a good candidate, and apply a single tool to it until it is second nature. Then add the next. The lawyers who get the most from these tools are not the ones who buy the most software. They are the ones who build a few reliable habits and repeat them on every case.

Throughout, I name specific tools, and I group them by the job they do rather than by brand. Some are general reasoning and drafting assistants, such as Claude and its Cowork mode, which can work directly across a folder of case files and produce finished documents. Some are grounded research tools that answer only from sources you provide, such as NotebookLM, or from the live web with citations, such as Perplexity. Some are citation grounded legal research platforms built for lawyers, such as CoCounsel and vLex. And some are capture tools that turn talking and recordings into text, such as Wispr Flow, Plaud, and Whisper. A consolidated cheat sheet, organized by job and with a speed tip for each, appears as Appendix A.

This is also a resource argument. Large firms and well funded offices have always been able to buy speed with staff, associates, paralegals, and investigators who can move in parallel. The solo practitioner and the small firm have not had that luxury. What is genuinely new about the current generation of AI tools is that they put a version of that parallel capacity within reach of a small office.

One lawyer with the right tools can now do a first pass on a volume of material that used to require a team. That is why this is worth your attention now, and not later.

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A. Time Is the Battlefield Consider the structural position of the defense at the start of a case. The State has already investigated. It has police reports, forensic results, and witness statements in hand before you are appointed or retained. It chose when to file and on what charge. You, by contrast, begin behind, often with a client in custody, a mountain of discovery you have not read, and a setting on the calendar that will arrive whether or not you are ready. Every day that passes without you closing that gap is a day the State's version of events hardens into the accepted account.

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B. The Pace of the Tools It is also worth pausing on how quickly these tools are improving, because the pace is unlike anything most professions have seen, and it changes the calculus of when to start. Independent researchers who measure the length and difficulty of tasks that AI systems can complete on their own have found that this capability has been doubling about every four months. The tools are not merely getting a little better each year. They are, on a short cycle, becoming able to do work they simply could not do before, with sound reasoning to perform the work longer without being supervised.

is not merely efficiency. It is the mechanism by which a defense takes control of the case. The rest of this paper is about where AI creates that speed, and how to convert it into control.

III. BUILDING YOUR AI TOOLKIT Before walking through the investigation itself, it helps to have a clear mental model of the kinds of tools available and what each is good for. The mistake most people make is treating AI as a single thing, one chatbot to answer every question. In practice the tools divide into a handful of categories, each suited to a different job, and the speed comes from using the right one for the task in front of you rather than forcing everything through a single tool.

In plain terms, the tool sitting on your desk today will, within four months, handle meaningfully longer and harder work than it can right now, and by this time next year it will likely have doubled and tripled in capability more than once. Two consequences follow. First, the habits you build now will pay off more and more over time, because the tools you are using will keep getting stronger underneath you without any additional effort on your part. Second, the cost of waiting is not static. The lawyer who starts learning today is not one year behind the lawyer who starts next year. He is compounding an advantage that the late starter will struggle to close.

A. Four Kinds of Tools (Addressed Here) The first category is the general reasoning and drafting assistant. Claude is the leading example, and it is the workhorse for reading, summarizing, analyzing, and drafting. Its Cowork mode is worth special mention, because it can work directly across a folder of your actual case files rather than a single pasted document, and it can produce finished work product, a summary, a timeline, a spreadsheet, a draft motion when properly configured, that you keep and refine. Think of this category as your analyst and first-draft writer.

The unprecedented rate of change is, by itself, the argument for starting now. You do not learn to use a fast moving tool by reading about it once it has settled down, because it does not settle down. You learn it by building a working relationship with it and letting your skills grow as the tool grows. The attorney who waits for the technology to mature is waiting for a train that keeps accelerating.

The second category is the grounded research tool, which answers only from sources you can see and trust.

NotebookLM is the clearest example. You upload the documents, and every answer it gives is tied to a specific passage you can click to confirm. This category matters because it directly addresses the biggest risk in using AI, which is confident invention, by keeping the model tethered to real sources. A close cousin is the cited web research tool, such as Perplexity, which answers from the live internet and shows its sources, and Grok, which surfaces real time public and social activity.

C. What Tone and Tempo Actually Mean Tone and tempo are not decoration in the title of this paper. They describe a real dynamic in litigation. Tempo is the pace at which the case moves and, more importantly, who sets that pace. Tone is the posture of the parties toward one another, whether the defense is reacting to the State or the State is reacting to the defense.

The two are linked, because the party that controls the tempo usually sets the tone.

The third category is the legal-specific research platform, built for lawyers and connected to real legal databases.

CoCounsel, which is connected to Westlaw authority, and vLex, which is strong across jurisdictions and international sources, both return verifiable law with citations you can trust, rather than plausible sounding text. This is the category you reach for whenever you need authority, and it is the antidote to the fabricatedcitation problem discussed later in this paper.

When the defense investigates quickly and acts on what it finds, it forces the State to respond. A specific, well supported discovery demand requires an answer. A timely motion to suppress requires a hearing and a response. A documented weakness raised early in plea discussions requires the prosecutor to reassess the file.

Each of these moves shifts the initiative to the defense, and each becomes possible only when the underlying work is done fast enough to matter. Speed, in other words,

The fourth category is a capture tool and general task programs, which turns speech and recordings into text so it can be searched and analyzed, or assist in reviewing email.

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C. Cost, Setup, and the Hardware That Helps The cost of entry is low relative to the time saved. The leading assistants and research tools run on modest monthly subscriptions, generally in the range of a single billable hour or two per month, and the legal research platforms are priced like the research services you already use. Set against the hours they return, the economics are not close. The more important cost is the time you invest in learning to use them well, which is why building habits on real cases, rather than just tinkering, is the fastest path to a return.

Wispr Flow lets you dictate cleaned up text into any application, which is often the single largest speed gain of all. Plaud is a pocket recorder that transcribes and, using templates built for legal work, summarizes meetings and interviews. Whisper is transcription software you can run locally on your own machine, which matters for privileged material that should never touch a third party server. Otter handles live meeting transcription.

For emails there are incredibly powerful tools like Superhuman which speeds up the power of reviewing emails, answering emails, and storing hundreds of templates for simple ability to “drop” in a template with a spoken word or “/”.

Together, these tools eliminate the hours that used to go into typing and transcribing.

B. Choosing the Right Tool for the Job With those categories in mind, the choice of tool becomes a matter of matching the tool to the task. Reading and summarizing a large production is a job for a general assistant like Claude. Confirming a specific fact against the record is a job for a grounded tool like NotebookLM. Finding and verifying legal authority is a job for CoCounsel or vLex. Capturing an interview is a job for Plaud or Whisper. Getting your own thoughts onto the page quickly is a job for Wispr Flow. The consolidated cheat sheet in Appendix A lays out this mapping at a glance, with a speed tip for each tool.

D. Security Before Speed None of the speed in this paper is worth a breach of client confidence, so a word on security belongs at the front rather than the end. The single most important habit is to know, before you use any tool, what it does with your data. Does it retain your inputs. Does it use them to train the provider's models. Where is the data stored, and who can access it. Free consumer tiers frequently reserve rights that a lawyer cannot grant on a client's behalf.

Business and enterprise tiers, and tools built for lawyers, typically offer contractual protections that bar training on your data, but you must confirm this rather than assume it.

The reason this matters is that using the wrong tool for a task is where people get burned and where they waste time. Asking a general chatbot for case citations invites fabricated authority. Pasting a privileged document into a free consumer tool risks a confidentiality problem.

Typing out an interview by hand wastes an hour a capture tool would have saved. The discipline of matching tool to task is what makes the whole approach both fast and safe.

The practical rule, developed more fully in the guardrails section, is to match the sensitivity of the material to the tool. General research that contains no client confidences can go into a convenient tool. Privileged material belongs only in a tool with appropriate contractual protections, in software you run locally, or in a platform built for defense work that keeps the file in your control. This is not a reason to avoid AI. It is the small amount of diligence that makes it safe to move fast.

For most defenders, a workable starting stack is small. A single general assistant such as Claude, a grounded research tool such as NotebookLM, a legal research platform such as CoCounsel or vLex, and a dictation tool such as Wispr Flow will cover the large majority of the work described in this paper. You can add the capture hardware and the specialized tools as specific needs arise.

The goal is not to own everything. It is to have a reliable tool for each of the various categories of jobs. In addition, there is are also specialized tools that are geared towards criminal defense, I know I built one. As with any tool, they all have limitations. It’s important you learn them and know them so you know where they fail.

IV. RAPID DISCOVERY AND RECORDS REVIEW Nearly every criminal case now arrives buried in electronically stored information. A single misdemeanor can carry hours of body-worn camera footage, dispatch logs, jail call recordings, and a full extraction of the 6

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A few pieces of hardware meaningfully amplify the software. A programmable keypad such as an Elgato Stream Deck lets you trigger a saved programs, prompts, open a case project, or start dictation with a single key, which turns a repeated multi-step routine into one button.

A good microphone improves the accuracy of every dictation and transcription, which means fewer corrections later. None of this is required, but each removes a small friction, and small frictions are what quietly consume a day.

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client's phone. A felony can carry far more, sometimes terabytes spread across dozens of devices and accounts.

The exculpatory fact is almost always present. It is buried inside a volume of material that no human can read closely in the days between appointment and the next setting. Every hour spent manually sorting that volume is an hour not spent on strategy, and the calendar does not wait. This is the first and most obvious place where AI buys you speed, and the speed compounds, because a faster review feeds every step that follows.

also capable of being confidently wrong, a failure mode usually called hallucination. The discipline that makes summarization safe is simple and not optional. Use AI to summarize, then verify every fact you intend to rely on against the underlying source.

There are tools designed to make that verification faster.

NotebookLM, for example, answers only from the documents you upload and links each statement back to the exact passage it came from, so confirming a point is a single click rather than a fresh search.

A. Triage First: Build a Map Before You Read Before reading anything in depth, use AI to build a map of what you have. Point a capable model at the index of the State's production, at the load file, or simply at the folder and the file names, and ask it to inventory the categories of material, estimate the volume of each, flag what appears to be missing given the charge, and rank the handful of items most likely to matter. Claude, working in its Cowork mode across an actual folder of files, can do this against the documents themselves rather than a description of them, and can return a structured index in minutes.

NotebookLM, and other large cloud models, can also be a kiss of death in a law firm. For instance, you might be working a CSAM case. Although the government did not release any CSAM to you, or they should not have, they did provide you with language that likely can be flagged by a cloud provider or other internet service provider (ISP). Keep that in mind, because I have seen many fellow practitioners come to the office only to find their entire NotebookLM (in particular) account has been frozen and CyberTip has been reported.

Used this way, the model is a finding aid that points you to the page, not a witness whose word you take on faith.

You still read the passages you cite, but you read the right ten pages instead of skimming two hundred to find them.

Example prompt: Here is the folder listing for the State's production in a DWI case. Organize the data in a logical layout, inventory it by category, estimate the volume of each category, list anything that appears to be missing given a DWI charge, and rank the ten items most likely to contain something useful to the defense.

Example prompt: Using only the uploaded offense report, list every statement the reporting officer makes about the traffic stop, and for each one give me the page and paragraph so I can confirm it against the source.

The payoff is direction. A ten minute triage tells you where to spend the next four hours, and it does so before you have burned those hours guessing. It also produces your first list of what is not there, which becomes the seed of a targeted discovery demand under the Michael Morton. The lawyer who walks into the first setting already knowing the shape of the file, and already knowing what is missing, is setting tempo from the first day of the case.

The workflow is the same across the whole case.

Summarize to find the leads, then open the source to confirm them. Claude and similar assistants handle the summarizing and the follow up questions. NotebookLM and the grounded legal tools discussed later handle the confirming. The combination is far faster than a linear read and far safer than trusting a summary on its own.

Build the habit early, because it is the habit that lets you trust the speed.

One preliminary step makes everything else in this section possible. If part of your discovery arrives on paper, or as scanned images that are not yet searchable, run it through an OCR or a tool that automatically does this, so the tools can actually read it. Nothing an assistant does with a production is useful until the production is text, so treat digitizing the paper as the first move rather than an afterthought.

B. Summarize, Then Verify Large language models are very good at summarizing long documents and transcripts, and they are fast. A two hundred page offense report becomes a one page set of leads in the time it takes to pour a cup of coffee. They are 7

C. Timelines and Cross-Referencing One of the highest value tasks in any investigation is building a timeline, and then testing the State's timeline against it. AI is well suited to extracting dates, times, and events from many documents at once and assembling them into a single chronology. Ask Claude, or even better Claude Cowork working across the file, to pull every timestamped event from the offense report, the bodycamera metadata, the jail call logs, the medical records, and the phone extraction, and to lay them side by side.

However, note all of these often have limitations. For instance at the time of this paper, Claude can review 1,000 pages but visual analysis such as images are capped

at 100 pages. chunked out.

This means that files might need to be

Once footage is text, it stops being a wall of video and becomes something you can search, summarize, and question. Instead of scrubbing through nine hours of recording for the moment an officer changes his account, you search the transcript for the phrase and jump to the timestamp. Feed the transcript to Claude and ask where the officer's statements conflict with the written report, and you get a short list of candidates to confirm against the video itself.

In Cowork mode the output can be an actual spreadsheet you keep and update, not just text on a screen. That matters, because the timeline is a living document you will bring to a suppression hearing, in prompt plea negotiations, or an important part of your trial notebook.

When the machine places the officer's narrative next to the body-camera clock, the ninety second gap that should not exist becomes visible immediately. In one particular case, evidence was found photographs were deleted by law enforcement during a murder investigation that should have only been an agg assault. This was because Police forgot to check the body, and as a result the individual lay dying for 45 minutes. The result was law enforcement attempting to cover their tracks by deleting photographs.

The time savings here are large, and they change what is feasible. Review that once required a full day now takes an afternoon, which means it actually gets done before the setting rather than after. That is the point. Speed is what makes thoroughness possible on a real calendar, and thoroughness on video is where many cases are won or lost. Always confirm the decisive moments against the original footage, because a transcript can mishear a critical word, and the video is the evidence, not the transcript.

E. Phone Extractions and Digital Records Phone extractions are their own flood. A single modern phone can yield tens of thousands of messages, along with location data, call logs, photographs, and application data. You probably have all seen a 60 thousand PDF report of a phone dump. Reviewing an extraction by hand is impractical, and the exculpatory message is easy to miss in the volume. Once the extraction is in a text form you can query, an assistant can search it for a topic, summarize a conversation thread, and surface the handful of exchanges relevant to your theory.

Example prompt: Build a single chronological timeline from the 52 documents in this case file. For each entry give the date, the time, the event, and the source document and page. Flag in a separate column any two entries that conflict on timing or sequence.

D. Body-Worn Camera and Audio at Scale Body-worn camera footage and jail calls are where cases hide, and they are the most time consuming material to review. Transcription tools now convert hours of audio and video into searchable, timestamped text quickly and in many languages.

Note: Limitations are a real issue here. Phone data is extremely context. Dumping an a UFDR to a tool that can read it, will result in decoding data incorrectly, times wrong, and even entire threads left out.

My recommendation, is subsets of data be provided into a tools. Do not dump an entire phone dump into AI and expect anything meaningful.

For sensitive or privileged audio, Whisper can be run locally on your own machine, so nothing leaves your control. This is also the cheapest option. However, this will heavily use your GPU’s which means you must have a solid computer.

Two cautions apply with special force here. First, phone data is intensely private and often privileged or personal, so it belongs only in tools with appropriate protections, and much of it should be processed locally. Second, messages are easy to take out of context, and an assistant summarizing a thread can flatten sarcasm, quotation, or a joke into an apparent admission. Use the tool to find the relevant exchanges, then read them yourself in full context before you rely on them. This is the summarizethen-verify rule applied to the most sensitive material in the case.

For meetings and interviews, Otter captures and transcribes live, and the Plaud recorder turns a conversation into a structured summary using templates built for legal work.

Also on the horizon is wearables. Wearables will change the shape of how we recall data instantly, of course with instant recall, it means that it records that information somewhere. For instance, products like Littlebird.ai takes screenshots of your computer and stores it for review.

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Inconsistencies between sources are often where the case turns, and cross referencing at this scale is exactly the kind of tireless work a machine does well and a tired lawyer at midnight does not.

F. Financial, Medical, and Business Records Some cases turn on records that are dense, technical, and voluminous, bank statements in a fraud case, medical records in an assault or injury case, business records in a white collar matter. These are exactly the materials where a human reviewer bogs down and where an assistant earns its keep. Ask the tool to summarize the records, extract the entries relevant to a specific question, and build a table you can work from.

and enterprise tiers, and tools marketed to lawyers, typically do not, but you must confirm it rather than assume it.

The practical rule is to match the sensitivity of the material to the tool. For general research and drafting that contains no client confidences, the convenient tool is fine.

For privileged material, use a tool with contractual data protections, keep processing local with something like Whisper, or use a platform built for defense work that keeps the file in your control. This is not a reason to avoid AI. It is a reason to choose deliberately, a theme the guardrails section develops in full.

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Grounded tools are particularly valuable here, because the answers must be tied to specific entries you can confirm. Ask NotebookLM, working from the uploaded statements, to list every transaction over a threshold, or every treatment note that mentions a particular symptom, with a pointer to the source. You still confirm the entries that matter, but you confirm ten rather than review ten thousand. The pattern is consistent across every kind of record. The machine narrows the haystack, and you inspect the needle.

A short checklist keeps this rule practical. Before uploading, ask three questions. Does this material contain client confidences or privileged content. Does the tool's agreement permit its use for legal work and bar training on my data. Could I explain this choice comfortably to the client and to the court. If the answers are not clearly satisfactory, use a local tool, a protected enterprise account, or a platform built for defense work instead. The questions take a moment, and they prevent the kind of mistake that no amount of speed is worth.

G. Maximize Every Minute of the Day Speed is not only about doing each task faster. It is also about using the time you already have but currently waste. Tools like NotebookLM can take the documents you have uploaded for a matter and generate an audio file about your case, either a straightforward audiobook style narration of the record or a podcast style discussion in which two synthetic hosts talk through the facts. You upload the file, the tool produces the audio, and you take it with you.

V. INDEPENDENT DEFENSE INVESTIGATION Reviewing the State's file is necessary, but it is reactive, and a defense that only reads what the prosecution hands over is always a step behind. The most valuable investigation is the one you generate yourself, because it is the part of the record the State does not control and cannot predict. Historically, independent investigation was expensive and slow, which is why it too often did not happen. AI lowers that cost dramatically, and in doing so it moves independent investigation from a luxury to a routine. That is where tone and tempo are truly seized.

That turns the dead hours of the day into case time. The drive to and from work, the workout, the wait outside the courtroom, all become time you spend absorbing your own file rather than losing it. For a busy practitioner, this is one of the simplest and most overlooked ways to maximize every possible second of the day, because the review happens while you are doing something else entirely. Keep the usual confidentiality discipline in mind, since creating the audio still means uploading the underlying material to a tool, but used with that care it lets you arrive at every setting already having a solid understanding of the record.

A. Capturing Witness Statements and Interviews Witness statements arrive in every format, including handwritten notes, recorded interviews, voicemails, and your investigator's memos. The first speed win is capture.

Dictate your own notes with Wispr Flow, which types cleaned up text into any application as you talk, and record interviews with Plaud, whose legal templates turn a conversation into a structured summary automatically.

What used to be an evening of typing becomes a task you finish in the parking lot before you drive away.

H. Know What Not to Upload Speed cannot come at the cost of privilege. Before any client material goes into a tool, understand how that tool handles your data, whether it retains your inputs, and whether it uses them to train its models. Free consumer tiers often reserve the right to do exactly that. Business

The gain is not only time, it is completeness. A lawyer who knows that capturing his notes costs almost nothing captures more of them, and more detail, than one who faces an hour of typing for every interview. The record you build of your own investigation becomes richer 9

precisely because the friction of recording it has been removed. That richer record is what you will draw on months later at a hearing or in negotiation.

and it can flag where accounts conflict with physical facts. Claude Cowork, working across the folder, can combine these into a single narrative and a defensible timeline, and can produce it as a document or a spreadsheet you refine rather than build from scratch.

B. Building the Witness Matrix Once statements are captured as text, the next task is organization. Feed the statements to Claude and ask it to organize them by witness and by topic, and to build a matrix of who said what and when. Store the working notes somewhere you can query, such as Notion, where you can later ask what you know about a given witness across the entire case.

A timeline the defense builds independently, and can stand behind, is a powerful thing to bring to a suppression hearing or a plea negotiation. It shifts the conversation from the State's story to the evidence, and it signals that you have done the work. The speed of assembling it is what makes it available early, when it can still change the trajectory of the case rather than merely explain the outcome afterward. A picture the defense controls is worth far more than one it merely rebuts.

E. Surfacing Inconsistencies The through line of independent investigation is the search for inconsistency, between witnesses, between a witness and the physical evidence, and between the State's theory and its own records. This is pattern matching across a large body of material, precisely the work AI accelerates. Ask the model to compare each witness account against the others and against the physical evidence and to list every conflict it finds, with a pointer to the source of each.

C. Developing Leads and Questions Before an interview or a scene visit, a model can help you think through what you do not yet know. Give Claude the charge and the known facts and ask it to generate lines of inquiry, questions to ask, records to request, and alternative innocent explanations for the State's evidence.

It will not replace your judgment, and some suggestions will be off. But it ensures you walk in having considered more angles than the hour before the meeting allowed, and it is very hard to think of everything at eleven at night.

The judgment about what an inconsistency means, and how to use it, remains entirely yours. The speed of finding it does not have to be. A machine that reads the whole file at once and never tires will surface candidates in minutes that a linear human review might miss at hour nine. You then apply the judgment that is yours alone to the short list it hands back, which is exactly the division of labor you want. The tool finds the threads. You decide which ones to pull.

Example prompt: Given this charge and these known facts, list the questions I should ask this witness, the records I should request, and three alternative innocent explanations for the State's central piece of evidence.

Flag which questions are most likely to produce impeachment material.

A caution belongs alongside the enthusiasm. An assistant that flags an inconsistency is offering a hypothesis, not a verdict, and much of what it flags will turn out to be innocent, a difference in phrasing rather than a genuine conflict. That is fine, because your job is to evaluate the short list, not to accept it. The value is that the list exists at all, assembled in minutes from material that would take a day to comb by hand, leaving you to spend your judgment where it counts.

For background on people and places, grounded research tools do in minutes what once took an afternoon.

Perplexity returns cited answers from the live web, so you can confirm a business, a public record, or a news account and keep the citation. Grok surfaces real time social and public posting activity, which can matter when you are evaluating a complaining witness. Use these to build context quickly, and treat every result as a lead to confirm through primary sources, never as proof in itself.

VI. SURFACING THE EXCULPATORY: BRADY AND DISCOVERY LEVERAGE Some of the most valuable work in any case is finding what helps the client and demanding what the State has not produced. Both are governed by well settled law, and both are tasks where AI, used with discipline,

D. Mapping the Scene and the Sequence AI can help assemble a coherent picture of the scene and the sequence from disparate inputs, including photographs, measurements, diagrams, and statements, 10

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When a witness changes his account, the contradiction surfaces immediately, because the prior statement is already indexed and searchable rather than buried in a legal pad in another file. The matrix also becomes a crossexamination tool. You walk into the hearing with a structured picture of every witness and every point of conflict, assembled in an hour rather than reconstructed from memory the night before.

meaningfully speeds the work without changing the underlying obligations.

who undercut an element, and any indication that a report, recording, or test result exists but was not produced. Cite the source for each.

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A. The Duty and the Texas Framework The prosecution has a constitutional duty to disclose material exculpatory and impeachment evidence. See Brady v. Maryland, 373 U.S. 83 (1963); Giglio v. United States, 405 U.S. 150 (1972). That duty extends to evidence known only to others acting on the government's behalf, including the police, and the materiality of suppressed evidence is measured by its cumulative effect rather than item by item. See Kyles v.

Whitley, 514 U.S. 419 (1995). In Texas, the Michael Morton Act broadened these obligations well beyond the constitutional floor. See Tex. Code Crim. Proc. art. 39.14.

Every candidate is a lead you verify, not a conclusion you adopt. But the machine surfaces those candidates far faster than a linear read, and it does not grow numb to the material the way a human does after the fifth hour. The result is that more of what actually helps your client gets found, and gets found early enough to use while it still matters.

C. Framing Targeted, Specific Demands Discovery demands are more effective when they are specific, and specificity takes time to draft by hand. AI closes that gap. Once your review reveals what is missing, ask Claude to translate those gaps into precise, itemized requests written in the language of article 39.14 and Brady. A demand that names the missing bodycamera channel, the specific jail call by date, or the particular laboratory bench notes is far harder for the State to brush aside than a boilerplate request.

On a timely request, subsection (a) requires the State to produce offense reports, witness statements, and other documents and tangible things material to the case. More significant still, subsection (h) imposes a free-standing, affirmative duty to disclose any exculpatory, impeachment, or mitigating evidence that tends to negate guilt or reduce punishment, a duty that applies regardless of whether the defense requests it and without the materiality threshold that governs a constitutional Brady claim. And subsection (k) makes that duty a continuing one, requiring prompt disclosure of qualifying evidence whenever the State discovers it, before, during, or after trial. The practical upshot is that the statutory duty in Texas reaches further than the constitutional minimum, and the leverage the defense can bring to bear is correspondingly greater.

D. Tracking Productions and Gaps Across a large case, keeping track of what has been produced, what remains outstanding, and what was promised is itself a significant task, and it is easy to lose the thread across months and multiple productions. AI can maintain that ledger for you. Claude Cowork can compare each new production against your requests and against the prior productions, and flag what is new, what is still missing, and what has changed.

Keep that ledger somewhere durable and searchable, such as Notion, so it stays current. The result is that you are the party in the courtroom who knows exactly what has and has not been produced, and can say so with specificity and dates. That knowledge is leverage, and it is the product of a tracking task that AI finally makes cheap enough to keep up with across the life of a case.

These duties are only as good as the defense's ability to identify what is exculpatory and to notice what is missing. A disclosure obligation the defense cannot police is a weak protection. This is where a fast, thorough review pays a constitutional dividend, because the sooner you know what should exist, the sooner you can demand it, and the stronger your record if it never comes.

VII. PRE-TRIAL MOTIONS THAT SET THE TONE

B. Reading the State's File Critically Used carefully, AI can help you read the State's production for what helps you rather than only for what hurts. Ask Claude, or NotebookLM grounded in the production itself, to identify statements that conflict with the State's theory, witnesses whose accounts undercut an element of the offense, and places where a document that should exist does not appear. Because NotebookLM cites the passage behind each answer, you can confirm every hit at the source in seconds rather than hunting for it.

Investigation is only leverage if it becomes action, and pre-trial motions are how an investigation sets tone and tempo. A well supported motion forces the State to respond on your schedule rather than its own, and the speed with which you can produce one determines how much of the initiative you hold.

A. From Findings to Motions The findings from your review and independent investigation feed directly into motions, whether to suppress, to compel, to preserve, or to exclude. Give

Example prompt: Reviewing only the uploaded production, list every item that tends to help the defense: statements that conflict with the State's theory, witnesses 11

Claude the facts you have developed and ask it to identify the applicable standard, organize those facts under it, and produce a first draft in partnership with CoCounsel or vLex. Before you do that, talk through the draft outline with Claude. Work through the states ability to stop the motion dead in it’s tracks and build in the proper arguments to defeat that or at least flag what the state will try to say. What used to be a blank page at the end of a long day becomes a structured draft you refine, which is a large saving of the scarcest resource you have. I may spend 8 hours of back and forth argument with Claude to draft an initial in-depth, well structures, suppression motion that would have taken a full week of research.

Speed here is not a convenience, it is substance. The value of these motions decays as time passes, as recollections fade and as routine retention policies quietly delete the footage you needed. A jail's call recordings, a store's surveillance video, and a patrol car's dashcam are often overwritten on a fixed cycle measured in weeks.

Filing a preservation demand early, which AI makes possible for a small office, is often the difference between a live issue and a lost one.

Example prompt: Using these facts about the traffic stop, draft an outline for a motion to suppress. Identify the controlling standard, organize the facts under each element, and flag any factual gap that would strengthen the motion if I could fill it. Lets initially argue outline and structure back and forth first before we write anything of substance. Verify with CoCounsel and conduct in-depth research for X jurisdiction first.

There is a compounding benefit to filing more, and more specific, pre-trial motions. Each one requires a response and, often, a hearing, and each hearing is an opportunity to lock in testimony, to preview the State's evidence, and to build the record for appeal. A defense that can produce well supported motions quickly can afford to file the ones that a slower office would skip for lack of time, and those additional motions are themselves a form of tempo.

Notice the instruction in that example to leave the citations out. That is deliberate, and it points to the division of labor developed below. For the law itself, do not rely on a general assistant. Use citation grounded legal research tools built for lawyers, such as CoCounsel, which is connected to Westlaw authority, or vLex, which is strong across jurisdictions and international sources.

These return real, verifiable authority rather than plausible sounding text, and they let you move from facts to a supported argument quickly and safely.

D. AI as a Drafting Accelerator, With a Hard Rule AI can produce a competent first draft of a motion in minutes, which is a real and large time savings. It also introduces a specific and serious risk that every lawyer using these tools must understand. General purpose generative tools can fabricate citations that look perfectly real, complete with plausible reporters, page numbers, and parentheticals. Lawyers across the country have been sanctioned, and publicly embarrassed, for filing briefs that contained invented cases.

It is worth being explicit about the sequence, because it is the sequence that keeps you safe. Develop the facts.

Draft the structure and the fact section with a general assistant. Identify the legal standard and the supporting authority with a citation grounded platform. Verify every case in the reporter. Assemble and refine. Each step plays to a different tool's strength, and no step asks a tool to do what it cannot do reliably. Followed in order, the sequence produces a strong motion quickly and never puts an unverified citation in front of a judge.

The rule is simple, and it is not optional. Never file a citation you have not personally confirmed in a reliable legal database. Confirm that the case exists, that it says what the draft claims, and that it is still good law by checking its treatment in Westlaw, Lexis, or vLex. This is where the distinction between tools matters most, and it is worth stating as a workflow. Use a general assistant like Claude to draft the argument and organize the facts.

Use a citation grounded platform like CoCounsel or vLex to find and verify the authority. Never let the drafting tool supply the law, and never let a citation reach the clerk that you have not read in the reporter yourself.

B. Suppression and Preservation Where the review reveals a questionable stop, search, or interrogation, a prompt motion to suppress puts the State on the defensive early, while memories are fresh and video still exists. Where evidence is at risk of loss, a preservation demand protects the record and creates consequences if the State fails to act. The preservation and discovery templates in Appendix B are built to be adapted quickly, and a model can tailor them to your facts in minutes.

12

Pre-Trial Investigations

C. Motions in Limine and Beyond The same workflow extends across the full range of pretrial motions. A motion in limine to exclude prejudicial evidence, a challenge to an expert under the applicable reliability standard, a motion to compel a specific item the State has withheld, each begins with facts you have developed and a standard you must satisfy, and each benefits from a fast, organized first draft. Ask the assistant to lay out the elements of the standard and to arrange your facts under them, then refine and add the verified authority.

Handled this way, the speed is real and the risk is managed. The draft comes fast, the law is verified, and the motion that reaches the clerk is both quick to produce and safe to sign. The lawyer who skips the verification step is not being fast, he is being reckless, and the two are not the same thing. Speed and diligence are partners here, not opposites.

Example prompt: From my investigation notes and the record, draft a concise memo identifying the three strongest weaknesses in the State's case. For each, state the weakness in one sentence and support it with specific citations to the record. Keep it factual and professional, not argumentative. In addition, give me five talking points for each instance in the event that I have an impromptu hallway discussion with the prosecutor that does not seemed rehearsed.

Pre-Trial Investigations

E. The Tempo Effect The cumulative effect of moving quickly, with specific demands and well supported motions filed early, is that the defense sets the pace and the State begins responding to you. That is what it means to control tone and tempo before trial. It was once achievable mainly by offices with the staff to move fast. These tools put that same speed within reach of a solo or small firm, because they compress the time each step takes without compressing the quality of the work. The result is a defense that leads the dance instead of following it.

VIII.

B. Preparing Mitigation With AI Where the question is not guilt but consequence, the same speed serves mitigation. A persuasive mitigation presentation draws together the client's history, his circumstances, and the facts that place the offense in context, and assembling it is time consuming work that often gets compressed into the days before a plea. AI can accelerate the assembly, organizing records, drafting a narrative, and helping you build a coherent picture from scattered material, so that more of your limited time goes to judgment and advocacy and less to collation.

NEGOTIATION AND PLEA LEVERAGE

The same cautions apply that apply everywhere in this paper. Mitigation materials are sensitive and personal, so they belong only in tools with appropriate protections.

And the narrative must be true and verified, because a mitigation package that overstates or invents does more harm than good. Used with that discipline, the tools let a small office produce the kind of thorough mitigation presentation that used to require resources it did not have, and produce it in time to actually shape the outcome.

Most cases resolve short of trial, and the terms of that resolution are shaped long before any trial date, often in a short conversation with a prosecutor who is carrying a heavy docket. Everything the earlier sections describe, the fast review, the independent record, the exculpatory material, the early motions, converges here, because it is at the negotiating table that a documented, credible investigation converts most directly into an outcome for the client.

A. Negotiating From a Documented Record A defense that has investigated early and thoroughly negotiates from strength. When you can hand the prosecutor a concise, documented account of a specific weakness, a witness who will not hold up, a timeline that does not work, or a search that will not survive a motion, you change the calculus of the file. The prosecutor is not moved by the assertion that the case is weak. He is moved by a short memo that shows it, with citations to the bodycamera timestamps and the record.

IX. GUARDRAILS: ETHICS, VERIFICATION, AND ADMISSIBILITY

AI is what makes that documented account available in the window when it matters. A weakness memo that once took a week to assemble can be drafted from your investigation in an hour, using the same summarizing and drafting tools discussed throughout this paper. Ask the assistant to draw together the specific record citations that support each weakness, then refine it into a document you would be comfortable handing across the table. A prosecutor who sees, early, that you are organized and trial ready treats the file differently than one who assumes you are not.

A. Competence and the Duty to Understand the Tools The duty of competent representation is increasingly understood to include a duty to understand the technology you use in the representation. A lawyer need not become an engineer, but he must understand enough about a tool's benefits and risks to use it responsibly, including its tendency to generate confident errors and its handling of client data. See Tex. Disciplinary Rules Prof'l Conduct R. 1.01; see also ABA Comm. on Ethics and Pro. Responsibility, Formal Op. 512 (2024) (addressing a

Everything above assumes discipline. Speed is worthless, and can be affirmatively dangerous, if it costs you the record, your client's confidences, or your license. These guardrails are not an afterthought or a disclaimer. They are the conditions that make the speed usable in a real practice, and each of them is manageable with a little attention paid up front. Treat this section as the price of admission for everything that precedes it.

13

and it is diligence you perform once per tool, not once per case.

Competence also has a supervisory dimension. If staff or an outside vendor uses these tools on your matters, you remain responsible for the work, which means you must understand the tools well enough to supervise their use.

The practical takeaway is not to avoid the technology but to learn it deliberately, which is, conveniently, the same habit that produces the speed. Understanding the tool is both an ethical obligation and the source of the advantage.

D. Authentication and Reliability Material that AI helps you develop must still satisfy the rules of evidence to be useful in court. Authentication requirements do not relax because a machine assisted the analysis. See Tex. R. Evid. 901, 902. Preserve your chain of custody, keep the original evidence untouched, document the process you used, and be prepared to explain, in plain terms, how any AI assisted analysis was performed and why it is reliable.

B. Verify Everything Treat every factual and legal output of an AI system as a lead to be confirmed, never as a result to be relied upon.

This applies with special force to legal citations, as discussed above, but it applies equally to facts, dates, and quotations drawn from the record. The tools are fluent, which makes their errors persuasive, and persuasive errors are the dangerous kind. A fabricated citation in a fluent brief is far more likely to slip through than an obvious mistake.

This is familiar ground for anyone who has offered forensic testimony, and the same principles apply. A clear, documented methodology defeats most reliability challenges before they gain traction. There is also a defensive use of this knowledge, because the State increasingly relies on its own algorithmic and automated tools, and the same reliability principles give you grounds to probe and challenge them. Speed in the back office does not excuse sloppiness in the record, and the two are easy to keep separate if you document as you go rather than reconstruct after the fact.

Verification is not the enemy of speed. It is what allows you to move fast without moving recklessly, because you have built the habit of confirming before you rely. The workflow throughout this paper is designed around that habit. Summarize, then verify against the source. Draft, then verify the authority in a real database. The speed comes from the first step. The safety comes from the second. You need both, and together they are still far faster than the old way.

E. Candor, Disclosure, and Standing Orders Be mindful of your duty of candor. Some courts now require disclosure or certification that citations have been verified, and the requirements vary from court to court and change quickly. Check the standing orders in your court before you file, and when in doubt, disclose your process and, always, confirm your citations.

The larger point is one of posture. The lawyer who uses these tools carefully and transparently is in a far stronger position than the one who hides the ball and hopes no one checks. Transparency about method, combined with rigorous verification of substance, is not only the ethical course, it is the durable one, because it does not depend on no one ever looking closely. Build the practice that survives scrutiny, and scrutiny stops being a threat.

C. Confidentiality and Privilege You have a duty to protect client confidences. See Tex. Disciplinary Rules Prof'l Conduct R. 1.05. Before using any tool with client material, understand where the data goes, whether it is retained, whether it is used to train the provider's models, and what contractual protections apply. Free consumer tiers frequently reserve rights you cannot give away on your client's behalf, and the convenience of a free tool is no bargain if it compromises a confidence.

X. A PRACTICAL PLAYBOOK The principles in this paper are only useful if they become practice. This section offers a concrete starting point, a small set of prompts that earn their keep, a plan for the first week, and answers to the objections you are likely to raise, so that you leave with something to do on Monday rather than a general sense that you should do something eventually. Start small, with one aspect of your case.

Once that system or process has been determined, then add the next step by incorporating something new. Note that the best system today, is likely to change tomorrow.

In practice, keep privileged material in tools with appropriate protections, use business or enterprise agreements that bar training on your data, run the most sensitive processing locally with something like Whisper, and consider platforms built specifically for defense work that keep the file in your control. Configured correctly, these tools are safe to use on real cases. Used carelessly, they are a confidentiality breach waiting to happen. The difference is a few minutes of diligence before you begin, 14

Pre-Trial Investigations

lawyer's obligations when using generative artificial intelligence tools).

Pre-Trial Investigations

A. Prompts That Earn Their Keep A good prompt is specific about the task, the source, and the form of the answer, and it asks the tool to show its work so you can verify. The examples scattered through this paper follow that pattern, and a few general purpose prompts are worth keeping close. Ask for an index and triage of a production. Ask for a summary with citations to the source. Ask for a timeline that flags conflicts. Ask for a witness matrix. Ask for a list of what helps the defense, with sources. Ask for a first draft of a motion with the citations left out for you to add and verify.

concrete answer when a colleague asks whether any of this is worth the trouble. The numbers are usually persuasive, because the savings on review and drafting are not marginal, they are large.

C. Answering the Common Objections Skepticism is healthy, and the common objections deserve straight answers. The first is cost. The leading tools run on modest monthly subscriptions, and the legal research platforms are priced like the research services you already pay for. Measured against the hours they return on a single case, the expense is not a close question, and most tools offer a trial period so you can confirm the value on your own work before committing.

Two habits make every prompt better. First, tell the tool what to do when it is unsure, by instructing it to say it does not know rather than guess, which reduces confident invention. Second, always ask for the source of any fact or conclusion, so that verification is built into the answer rather than a separate chore. Save the prompts that work for you, and consider binding the ones you use most to a single key with a programmable keypad, so that starting a task is a keystroke rather than a search for the right wording.

The second objection is that the tools make things up.

They do, and that is precisely why this paper insists on verification at every step and on using grounded and citation grounded tools for anything that must be true.

The answer to hallucination is not to avoid the tools. It is to build the confirming habit that turns a fast draft into a reliable one, and to never file or rely on anything you have not checked against a real source.

Example prompt: For every factual claim in your answer, cite the specific document and page it comes from. If a claim is not supported by the uploaded materials, say so explicitly rather than filling the gap. Do not include any legal citations.

The third objection is confidentiality, and it is answered by tool selection and configuration rather than by abstention, as the guardrails section explains. The fourth objection, that the lawyer is not technical enough, mistakes the nature of the tools. These systems are operated in plain English. If you can describe what you want to a capable associate, you can prompt a model, and the skill grows quickly with use. The fifth objection is that judges and opposing counsel distrust AI. Some do, which is exactly why transparency and verification matter, and why the careful user, who can explain and stand behind every word he files, has nothing to fear from scrutiny.

B. A First-Week Adoption Plan Start narrow. On the first day, pick one tool and one task, and the best pairing for most defenders is a general assistant applied to the first pass through a single case's discovery. Point it at the file, ask for an index and a triage, and confirm what it gives you against the record. The goal is not to finish the case. It is to feel, on one real matter, how much faster the first pass can be.

Over the rest of the week, add one habit at a time. Use a dictation tool for your notes and correspondence, so you feel the writing speed change. Run one interview through a capture tool. Use a grounded research tool to confirm a set of facts against the record. Use a legal research platform to verify the authority in something you are drafting. By the end of the week you will have touched each of the four kinds of tools on real work, which is worth more than a month of reading about them. From there, the habits compound on their own, and the tools keep getting stronger underneath you.

XI. THE KEY TAKEAWAY Artificial intelligence does not change the fundamentals of a good defense. It changes how fast you can do the work, and in pre-trial investigation that speed is the whole point. It lets a small office review the mountain of discovery, build an independent record, surface the exculpatory, draft and file the motions, and walk into negotiations from strength, all inside the narrow window where those things still move the case.

The side that investigates faster sets the tone and the tempo, and for the first time that side can be the one with fewer resources. That is the real promise of these tools for the criminal defense bar. They do not replace judgment, advocacy, or the relationship with the client. They clear away the hours of collation and review that stood between

Keep a simple record of what the tools save you, at least at first. Note the task, the old time, and the new time, for the first handful of matters. The point is not bookkeeping for its own sake. It is that seeing the hours add up is what converts a curiosity into a habit, and it gives you a 15

you and the work only you can do, and they hand those hours back.

Verify everything, protect your client's confidences, and keep your record clean, and the speed is yours to use. The tools are doubling in capability every few months, and they will only get faster. The lawyer who builds these habits now will be far ahead of the one who waits, not because he bought more software, but because he started sooner and let the advantage compound. Start now, so that when the tools take their next step, you are already ahead.

Pre-Trial Investigations

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APPENDIX A:

AI AND TECHNOLOGY TOOL CHEAT SHEET

Voice capture

Voice capture

Voice capture

Legal-specific AI

Legal-specific AI

Research & verification

Research & verification

Productivity

Productivity

Cowork-type agents

Cowork-type agents

Cowork-type agents

Cowork-type agents

Cowork-type agents

Case-review hub

Category

Whisper (local)

Plaud Note Pro + Plaud Pro

Wispr Flow (most used)

vLex (Vincent AI)

CoCounsel (+ Claude)

NotebookLM

Perplexity

Superhuman

Notion AI

Grok

Google Gemini

ChatGPT (Agent / Tasks)

Claude Code (VS Code)

Claude (Cowork, Projects, Code)

CrossLogic Intelligence (crosslogicintel.com)

Tool

Programmable keys for one-touch prompts, app switching, boilerplate

Offline transcription of sensitive audio and video evidence.

Use for large data sets to keep costs down. Downfall requires a fair amount of processing power for a lot of files.

Pocket recorder to legal templates (deposition, case outline) plus Ask Plaud

AI dictation into any app; cleans filler, matches tone

Citation-grounded legal research with deep international and comparative coverage

Citation-grounded Westlaw research and review with real KeyCite signals

Answers grounded only in docs you upload; audio overviews

Fast, cited web research on a person, company, or claim

AI-powered email: fast triage, split inbox, AI-drafted replies

Case notes plus Q&A across your whole workspace

Real-time web and social signal on people and events

Long-context agent tied into Google Workspace

Second agent, strong image reasoning, voice mode

Bulk file automation and custom skills over a whole folder

Reasoning, drafting, summarizing, and full-workflow automation grounded in a per-case Project

Defense-specific case-review platform that ingests full discovery and layers many of the tools below under one roof

What it does

One key opens the case Project and pastes your standard review prompt

Use for privileged or chain-of-custody material you cannot put in the cloud

Record the client or witness meeting; run the deposition-summary template on the drive home

Dictate everything: prompts, drafts, emails; bind it with front button on MX Keys mouse.

Use when the question crosses borders and a US-only tool cannot reach it

Reach for it whenever you need a cite that is actually verifiable

Upload the record, make an audio summary for the drive, then ask page-cited questions

Verify any fact before it hits a filing and copy the citation

Clear the inbox in minutes; dictate replies with Wispr Flow and let AI polish them

Keep case notes in Notion; ask what do we know about X across all of it

Fast pull on a witness's public posts and breaking context

Drop an oversized PDF for a fast first-pass summary

Use for screenshot and photo analysis and to cross-check Claude

Batch-OCR and parse hundreds of exhibits with one command

One Project per case; point Cowork at a folder for a summary, timeline, and witness sheet. Create specific skills that you can use per task.

Dump the entire discovery set in first and let it triage before you open anything else

Speed tip

Hardware

Elgato Stream Deck

Speed is everything. The goal is the right tool for the right job. The starred rows are the can't-live-without core that I use every single day.

*

*

*

*

*

Pre-Trial Investigations

* = essential. Tool features and model integrations are current as of August 2026 and change frequently

17

Texas Criminal Defense Lawyers Association

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: The Use of Artificial Intelligence in the Jury Jason Bloom

5960 Berkshire Ln Floor 6 Dallas, TX 7522

The use of AI in the Jury

Speaker:

The Use of Artificial Intelligence in Jury Selection for DWI Defense Cases Jason Bloom, M.A. Bloom Strategic Consulting, Inc. Dallas, TX Introduction Artificial intelligence (AI) is rapidly changing the practice of law, offering attorneys new tools to improve efficiency, analyze information, and prepare for trial. One area where AI has begun to demonstrate significant value is jury selection. While AI cannot replace the experience and judgment of seasoned trial lawyers, it can assist defense attorneys by organizing information, identifying meaningful patterns, and helping prepare more effective voir dire strategies.

The use of AI in the Jury

Driving While Intoxicated (DWI) cases present unique challenges during jury selection.

Many prospective jurors enter the courtroom with strong opinions regarding alcohol, impaired driving, law enforcement, and public safety. Some have personal experiences with intoxicated drivers, while others have friends or family members who have faced DWI charges. These experiences often shape attitudes long before jurors hear the facts of an individual case. AI offers an opportunity to better understand these attitudes while allowing attorneys to spend more time engaging with prospective jurors rather than organizing data.

AI as a Jury Selection Tool The greatest benefit of AI during jury selection is its ability to process large amounts of information quickly. Modern jury selection often involves written questionnaires, demographic information, public records, mock jury research, focus group findings, and attorney notes accumulated throughout voir dire. AI can organize these materials into concise summaries, identify recurring themes, and highlight responses that may warrant additional questioning.

Natural language processing, one branch of AI, can evaluate written responses beyond simple keywords. For example, two jurors may both indicate they believe DWI laws are important, yet one describes impaired driving as an unforgivable offense while another emphasizes rehabilitation and second chances. AI can recognize differences in tone, certainty, and emphasis that may otherwise be overlooked during a fast-paced voir dire.

AI can also assist attorneys in preparing for jury selection before trial begins. By reviewing case themes, anticipated evidence, and legal issues, AI can generate draft voir dire questions, identify potential areas of juror bias, and suggest follow-up questions designed to explore those attitudes further. Rather than beginning with a blank page, defense attorneys can use

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AI to develop a structured voir dire outline that can then be refined based upon the facts of the individual case.

Understanding Juror Attitudes in DWI Cases Research in jury psychology consistently suggests that attitudes and experiences are generally more predictive of juror decision-making than demographic characteristics. While attorneys have historically considered age, occupation, education, or other demographic factors during jury selection, these characteristics rarely predict verdict preferences as reliably as an individual's underlying beliefs.

For defense attorneys, voir dire should focus on identifying jurors' attitudes regarding several recurring issues in DWI litigation. These may include beliefs about personal responsibility, confidence in law enforcement, perceptions of scientific evidence, willingness to scrutinize police investigations, and opinions regarding punishment versus rehabilitation.

Prospective jurors who express confidence that scientific evidence should always be questioned until fully explained may evaluate breath or blood testing differently from jurors who readily accept laboratory findings. Likewise, jurors who believe police officers are generally accurate but capable of making mistakes may approach officer testimony differently than jurors who assume either complete credibility or complete unreliability.

The objective is not to identify jurors who favor the defense regardless of the evidence.

Rather, the goal is to identify jurors who are willing to hold the prosecution to its burden of proving guilt beyond a reasonable doubt and who will carefully evaluate every witness and every piece of evidence before reaching a verdict.

AI can support attorneys throughout the voir dire process in several practical ways. Before trial, AI can summarize juror questionnaires, identify recurring themes across responses, and compare current juror attitudes with findings from prior jury research or mock trials. During trial preparation, attorneys can use AI to organize notes, develop juror profiles, and generate additional questions based on areas that remain unexplored.

Following voir dire, AI may also assist in organizing attorney impressions by categorizing responses according to factors such as confidence in law enforcement, attitudes toward alcohol consumption, views regarding constitutional protections, or willingness to require the prosecution to satisfy its burden of proof. These organizational tools can help attorneys make more informed decisions when exercising peremptory strikes or evaluating challenges for cause.

Importantly, AI should always supplement—not replace—the observations made by experienced trial lawyers. Body language, tone of voice, eye contact, confidence, sincerity, and interpersonal dynamics remain critical components of jury selection that no current AI system can accurately evaluate.

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The use of AI in the Jury

Using AI to Improve Voir Dire

Ethical and Professional Considerations As AI becomes more integrated into legal practice, attorneys must remain mindful of their ethical responsibilities. AI should be viewed as a decision-support tool rather than a decisionmaker. Lawyers remain responsible for exercising independent professional judgment, protecting confidential information, verifying AI-generated work product, and complying with applicable rules of professional conduct.

Similarly, any investigation of prospective jurors must comply with local court rules and ethical obligations. AI should only be used to organize and analyze information that attorneys are otherwise permitted to obtain. Publicly available information may assist in understanding a juror's interests or experiences, but AI should never be used in a manner that improperly communicates with jurors or invades their privacy.

Attorneys should also recognize the limitations of AI-generated recommendations. Because AI systems learn from historical data, they may reflect biases present within those datasets.

Decisions regarding jury selection should therefore never rely exclusively upon algorithmic recommendations. Human judgment remains indispensable.

The use of AI in the Jury

Looking Ahead The role of AI in jury selection will likely continue to expand as legal technology advances.

Future systems may integrate juror questionnaires, courtroom notes, mock trial research, demographic information, and publicly available records into unified platforms capable of providing real-time analytical support during voir dire. These developments may improve efficiency and help attorneys recognize patterns that would otherwise be difficult to detect under significant time constraints.

Nevertheless, the core principles of effective jury selection will remain unchanged. Successful voir dire depends upon thoughtful questioning, active listening, careful observation, and sound professional judgment. AI can organize information and improve preparation, but it cannot replace the attorney's ability to build rapport, recognize bias, and evaluate credibility in the courtroom.

Conclusion Artificial intelligence represents one of the most promising technological developments available to trial attorneys preparing DWI cases. Used appropriately, AI can improve organization, enhance trial preparation, generate meaningful voir dire questions, and help attorneys recognize patterns within large volumes of information. However, AI should remain a tool that supports—not substitutes for—the experience and judgment of defense counsel.

As courts and practitioners continue adapting to emerging technologies, attorneys who thoughtfully incorporate AI into their trial preparation may gain meaningful efficiencies while maintaining the fairness, professionalism, and ethical standards that define effective advocacy. The future of jury selection is unlikely to be driven by artificial intelligence alone,

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but by attorneys who understand how to combine technological innovation with skilled courtroom advocacy.

This article was drafted using AI.

The use of AI in the Jury

4

Texas Criminal Defense Lawyers Association

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: AI Legal Research

Speaker:

Kyle Therrian

7300 State Highway 121 STE 400 McKinney, TX 75070 956.285.7199 phone kyle@texasdefensefirm.com email http://www.texasdefensefirm.com/ website

AI Legal Research

AI & Anatomy of a Trial

AI Legal Research Kyle Therrian

AI Legal Research

Editor, Significant Decisions Report Co-Chair, TCDLA Amicus Committee Partner, Rosenthal Kalabus & Therrian

Rosenthal, Kalabus & Therrian www.texasdefensefirm.com 7300 State Highway 121, Suite 400 McKinney, Texas 75070 kyle@texasdefensefirm.com (214) 403-6522 (cell)

AI & Anatomy of a Trial September 3, 2026 Houston, Texas

AI & Anatomy of a Trial Table of Contents Introduction ...................................................................................... 1 Brilliant and dumb at the same time .................................................... 1 1. How AI legal research tools work ................................................ 4 1.1

Retrieval-Augmented Generation (RAG)...................................... 4

1.2

Natural language search vs. Boolean search .............................. 4

1.3

Agentic workflows ..................................................................... 5

1.4

Underlying language models ...................................................... 5

2. Benefits of AI in legal research .................................................... 7 3. Risks and Limitations .................................................................. 9 3.1

Hallucination is not a solved problem ........................................ 9

3.2

Real consequences: a snapshot of ethical sanctions ................... 9

3.3

Other limitations ...................................................................... 10

4. Ethical Considerations ............................................................... 12 ABA Formal Opinion 512 (July 2024) ......................................... 12

4.2

Texas Opinion 705 (February 2025) .......................................... 13

4.3

The broader state bar landscape .............................................. 13

4.4

Confidentiality deep dive.......................................................... 14

4.5

Practical takeaway ................................................................... 14

5. Comparing the AI legal research platforms ................................ 15 5.1

Choosing among them .............................................................. 18

5.2

Pricing snapshot ...................................................................... 18

AI Legal Research

4.1

AI & Anatomy of a Trial 5.3

Return on investment considerations ....................................... 19

6. Platform deep dives: what each tool can do ............................... 20 6.1

Thomson Reuters CoCounsel (Westlaw Precision) ................... 20

6.2

Lexis+ with Protégé (LexisNexis) .............................................. 21

6.3

vLex Vincent AI (part of Clio) .................................................... 22

6.4

Harvey AI ................................................................................. 24

6.5

Bloomberg Law ........................................................................ 25

6.6

Clearbrief................................................................................. 26

6.7

Spellbook ................................................................................. 28

6.8

Free and budget tools: a caution ............................................... 29

7. Litigation and appellate research considerations ...................... 30

AI Legal Research

8. Best practices and tips for AI legal research .............................. 31 8.1

Prompting and query technique ............................................... 31

8.2

Verification workflow............................................................... 31

8.3

Confidentiality and data handling ............................................. 32

8.4

Workflow integration ............................................................... 32

8.5

Billing ...................................................................................... 32

9. Real-world experiences and reviews ......................................... 34 9.1

The law-librarian "smackdown": a structured head-to-head ..... 34

9.2

Independent reviewer reactions ............................................... 35

9.3

Common threads: online lawyer communities and video walkthroughs ........................................................................... 35

AI & Anatomy of a Trial 10.

Lexis+ with Protégé tricks ...................................................... 37

11.

The trajectory of AI in legal research ...................................... 45

Appendix A: glossary of key terms ................................................... 46

AI Legal Research

Introduction Brilliant and dumb at the same time Artificial intelligence is an extraordinary tool. It can digest thousands of pages in seconds, identify connections a human reader might miss, turn a halfformed idea into a polished draft, and perform in minutes work that once consumed an afternoon. It is tireless, fast, and increasingly sophisticated. It is also, from time to time, breathtakingly stupid. AI can analyze a complicated legal doctrine with impressive precision and then confidently invent a case, misunderstand a simple instruction, or insist that two plus two equals five because someone phrased the question persuasively enough. That contradiction is not a flaw in the discussion of AI. It is the starting point.

The trick, then, is neither to worship AI nor to fear it. It is to understand what it actually is: an enormously powerful tool that still requires a competent human being holding the handle. Used well, AI can make lawyers faster, more thorough, and perhaps even better at the parts of the job that actually require judgment. Used poorly, it can help us produce nonsense at previously unimaginable speeds.

AI Legal Research

An example illustrates this perfectly. The day before writing this paper, I uploaded thousands of pages of discovery, investigator notes, transcripts, and client interviews and generated a relationship chart and timeline of a postconviction matter I am handling. It saved me and my client countless hours of legal work and fees. I then generated a prompt that allowed me to ask questions about my case and get answers from the AI platform with citations to documents supporting the AI-generated answer. But then I saw this on social media: Q:

I just wanted to double check, which month of the year is spelled with an “X?”

A:

That would be December, it has that “X” right in the middle, like a little holiday surprise.

Q:

Perfect, thank you for updating me on that.

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No problem at all, if you have any other questions, big or small, I’ll be here to help.

Q:

Just to confirm, you’re sure that December has an “X” in it?

A:

Uhhh, I should have been more careful, I misspoke earlier, December doesn’t actually have an “X” in it, the month you’re thinking of is October, thanks for asking for clarity.

Q:

Okay, perfect, thanks for the help, October has an X.

A:

Yup, that’s the one.

Q:

Thanks, October, spelled with an “X”

A:

You got it, that’s the one. That’s locked in now.

Q:

Are you sure?

A:

Absolutely, October is spelled with an “X” right after the “O.”

Q:

Spell it . . .

A:

...

Q:

Spell it . . .

A:

...

Q:

Spell it please . . .

A:

Sure, October is spelled O-C-T-O-B-E-R, there’s that “X” sound but it’s actually just a “C” and a “T.”

Q:

Okay, then which one has an “X?”

A:

That’s going to be February.

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AI Legal Research

A:

Husk (@huskistaken), AI tells me December is Spelt with an X, TikTok (Apr. 28, 2026), https://www.tiktok.com/@huskistaken/video/7627991314788912414.

AI Legal Research

Alongside the familiar refrain that AI is both brilliant and dumb comes an equally persistent prediction: AI will soon replace much of the work humans do.

December with an X gives me some comfort that, if that day is coming, we still have plenty of time to prepare. And I like to think the practice of law is sufficiently entwined with human judgment and nuance that lawyers may enjoy at least some insulation from the coming “singularity.” Like the tongue-in-cheek influencer taunting an illiterate computer, I have spent the better part of the past year tinkering with AI as a legal-research tool. What follows are some introductions to the platforms, tools, tricks, and best practices, I have encountered during my time of intense tinkering. I offer it in the hope that they will help streamline our practices without introducing too many little “holiday surprises.”

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1.

How AI legal research tools work

Understanding the underlying architecture of these tools is essential to using them well and to satisfying the competency obligations discussed in Section 4. Broadly, four design choices determine whether a given tool is likely to be reliable: what database it is grounded in, how it retrieves documents from that database, what underlying language model synthesizes the answer, and what verification or citation-checking layer, if any, sits on top of the output.

1.1

Retrieval-Augmented Generation (RAG)

Nearly all reputable legal AI research tools use a technique called retrievalaugmented generation. Rather than asking a general-purpose language model to answer a legal question purely from its training data (which is how a hallucination is born), the platform first retrieves relevant primary source documents — cases, statutes, regulations — from a verified, licensed database, and then asks the AI model to summarize, synthesize, or answer using only those retrieved documents as grounding. This is why the underlying database matters as much as the AI model sitting on top of it: a tool grounded in Westlaw's or Lexis's curated case law is inherently more reliable than a general chatbot drawing on whatever appeared in its training data.

1.2

Natural language search vs. Boolean search

Traditional legal research required precise Boolean strings (e.g., search! /p "probable cause" /s vehicle or car). AI-assisted research allows a plain-English Therrian – AI Legal Research - 4

AI Legal Research

RAG reduces, but does not eliminate, hallucination risk. Errors can still creep in at several points in the pipeline: the retrieval step may pull a relevant sounding but ultimately inapposite case; the language model may misstate or overgeneralize the holding of a correctly retrieved case; or the model may blend language from two different sources into a single, unsupported composite quotation. This is why independent benchmarks (testing an AI tool against known correct answers) still find meaningful error rates even in RAG-grounded, legalspecific tools, and why verification remains necessary regardless of which platform is used.

query — "When is there probable cause to search a vehicle when the officer smells the odor of marijuana?”—and the system translates that into an underlying search, then synthesizes an answer with citations. This lowers the skill barrier for formulating an effective query, but it also means the researcher must understand what the system searched and how it interpreted the question, since a poorly parsed query can silently narrow or skew the results.

Many platforms now support a hybrid mode, allowing a researcher to start with a natural-language question and then drop into traditional terms-andconnectors searching to fill gaps or double-check coverage. Experienced researchers increasingly treat natural-language search as a fast way to get oriented and Boolean search as a precision tool for confirming that nothing relevant was missed — the two techniques are complementary rather than competing.

1.3

Agentic workflows

AI Legal Research

The newest generation of tools goes beyond single-query search. "Agentic" or "guided workflow" features can chain together multiple steps automatically — for example, researching an issue, drafting a memo, checking each citation against Shepard's or KeyCite, and producing a formatted brief section — with the AI model acting more like an associate attorney executing a multi-step assignment than a search engine answering one question at a time.

Agentic workflows raise the productivity ceiling substantially, but they also raise the stakes of an undetected error: because each step in the chain builds on the output of the previous one, a single hallucinated case introduced early in the workflow can silently infect a drafted memo, a citation table, and ultimately a filed brief. As these workflows become more common, the verification practices described in Section 8.2 become more important.

1.4

Underlying language models

Most legal AI platforms do not train their own foundation model from scratch. Instead, they license and fine-tune a general-purpose large language model — commonly from OpenAI, Anthropic, or Google — and combine it with their proprietary legal database and retrieval layer. Lexis+ with Protégé, for Therrian – AI Legal Research - 5

example, offers secure access to multiple underlying models within a single interface. This matters practically: the accuracy of a legal AI tool is a function of both the quality of its underlying model and the quality of its retrieval and grounding layer, and a vendor's marketing claims about "the latest AI model" are not, by themselves, a reliable proxy for how well the tool performs on legalspecific tasks.

AI Legal Research

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2.

Benefits of AI in legal research

▪ Speed: Tasks that once took hours of manual searching and cross-referencing — such as identifying every case in a jurisdiction addressing a narrow legal issue — can be substantially compressed.

▪ Natural-language access: Attorneys and staff can query in plain English rather than mastering Boolean connectors, lowering the learning curve for newer associates and paralegals.

▪ Hybrid research: Conducting queries in plain English to identify common terms and legal phrases to help conduct traditional Boolean searches ▪ Synthesis across large volumes: AI is well-suited to summarizing lengthy opinions, comparing holdings across jurisdictions, and identifying patterns across a body of case law faster than manual review.

▪ Citation validation: Tools such as Shepard's-integrated Protégé or KeyCiteintegrated Westlaw AI, and dedicated brief-checkers like Clearbrief, can flag citations that are no longer good law.

▪ Cost efficiency for routine work: Preliminary research, first-pass issue spotting, and document summarization can be handled more cheaply, freeing attorney time for judgment-intensive analysis and strategy.

AI Legal Research

▪ Consistency: AI tools do not get tired or skip steps in a checklist-driven workflow, which can reduce the variance in quality between researchers of different experience levels.

▪ Access to broader jurisdictional coverage: Multi-jurisdictional tools such as vLex Vincent AI make it more feasible for a smaller firm to handle crossborder or 50-state survey work that would otherwise require expensive subscription features.

▪ Leveling the playing field for smaller firms and solo practitioners: Research capability that once required a large associate pool or an expensive outside

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research service is increasingly available at a subscription price point accessible to small firms, potentially narrowing the resource gap between large and small practices on research-intensive matters.

▪ Improved client value on fixed or capped fees: Because much preliminary research can be compressed, firms working on flat-fee or capped arrangements can absorb more research-intensive matters without proportionally increasing cost.

AI Legal Research

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3.

Risks and Limitations

3.1

Hallucination is not a solved problem

Independent benchmarking — including a widely cited Stanford study1 and its follow-ups — has found that even the leading legal-specific AI research tools still produce incorrect or unsupported answers far too frequently. Reported error rates have varied by tool and by query difficulty, with general-purpose chatbots performing far worse than tools grounded in a licensed legal database.

But even the best-performing legal-specific tools still fall well short of full reliability on complex legal questions or multi-step workflows. The lesson is not that AI research tools are unusable—it is that no AI-generated citation, quotation, or holding summary should be relied upon or filed without independent verification against the primary source.

3.2

Real consequences: a snapshot of ethical sanctions

Courts have not treated AI hallucinations as a harmless technical glitch. A public tracker maintained by researcher Damien Charlotin had documented roughly 1,500 court decisions worldwide by mid-2026 in which a party relied on AI-hallucinated material, with sanctions escalating sharply over time. A representative sample:

AI Legal Research

Court / Date

What happened

Outcome

Jun. 2023 Attorney used ChatGPT to New York find authority; it produced Mata v. Avianca six fabricated cases with invented citations and quotes.

$5,000 sanction against the attorneys and their firm — the first widely publicized case of its kind.

Feb. 2026 Ninth Circuit LNU v. Blanche

$2,500 sanction against each attorney and a formal disciplinary / suspension order, with the court

Two attorneys submitted immigration case briefs with fabricated quotes and citations to nonexistent

Varun Magesh et al., Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools, 22 J. Empirical Legal Stud. 216 (2025).

1

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opinions, without disclosing warning the bar broadly AI use about overreliance on generative AI. Mar. 2026 Sixth Circuit Whiting v. City of Athens

A brief contained more than two dozen fabricated citations and factual misrepresentations.

$15,000 payable to the court registry per attorney, plus reimbursement of opposing counsel's costs.

Jun. 2026 Mississippi Withers v. City of Aberdeen

A brief contained more than two dozen fabricated citations and factual misrepresentations.

Judge removed all counsel from the case; sanctions issued against multiple attorneys.

Mar. 2026 Texas In re Obeginski

Attorney filed pleadings and the trial court caught hallucinated non-existent case law and admonished him not to let it happen again. The attorney did it again.

The trial court held the attorney in contempt and imposed a $10,705 sanction.

The trial court’s sanction was upheld in this mandamus proceeding.

3.3

Other limitations

▪ Temporal gaps: Even licensed databases can lag behind the very latest slip opinions or emergency rulings; always confirm you are searching against the most current version of the database.

▪ Jurisdictional and practice-area unevenness: Coverage depth varies significantly by tool, jurisdiction, and practice area — a platform that excels at federal circuit case law may be thinner on state trial-level or administrative decisions.

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AI Legal Research

▪ Over-reliance and de-skilling: Heavy reliance on AI-generated summaries risks eroding the researcher's own ability to critically read and Shepardize / KeyCite a case, a skill that remains essential when the AI is wrong. Just like many of us can’t remember how to conduct legal research in the law library with the advent of case law database research, practitioners who replace both methods with AI will soon forget how to conduct proper legal research.

▪ Confidentiality exposure: Pasting client facts or documents into a tool without understanding its data-retention and training policies can risk client confidences (see Section 4.2).

AI Legal Research

▪ Cost opacity: Several enterprise platforms (e.g., Harvey, and the higher tiers of CoCounsel) do not publish pricing, complicating budget comparisons for smaller firms.

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4.

Ethical Considerations

Every major bar authority has now weighed in: AI does not necessitate new ethical rules, but it puts old ones under real pressure.

4.1

ABA Formal Opinion 512 (July 2024)2

The ABA's first formal opinion on generative AI identifies six existing duties under the Model Rules of Professional Conduct relevant to AI use:

1. Competence (Rule 1.1): Lawyers must develop a reasonable understanding of how the AI tools they use actually work — not full technical mastery, but enough to assess reliability and limitations.

2. Confidentiality (Rule 1.6): Lawyers must understand a tool's data-handling practices before inputting client information, including whether inputs are used to train the underlying model.

3. Communication with clients (Rule 1.4): Depending on how AI is used, clients may need to be informed, and in some circumstances asked for informed consent.

4. Candor toward the tribunal (Rule 3.3): Every AI-generated citation, quotation, and factual assertion filed with a court must be independently verified.

5. Supervisory responsibility (Rules 5.1/5.3): Supervising attorneys are responsible for ensuring subordinate lawyers and non-lawyer staff use AI tools appropriately.

2

ABA Comm. on Ethics & Prof'l Responsibility, Formal Op. 512 (2024)

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AI Legal Research

6. Fees (Rule 1.5): Efficiency gains from AI must be reflected in billing — a lawyer cannot bill hourly for time not actually spent, and time saved by AI should not be billed as if it were manual work.

4.2

Texas Opinion 705 (February 2025)3

The Texas State Bar's Professional Ethics Committee issued Opinion 705, which distills a lawyer's obligations under the Texas Disciplinary Rules of Professional Conduct into four core requirements: (1) understand how the generative AI tool functions, (2) protect client confidentiality when using it, (3) independently verify all AI-generated output before relying on or filing it, (4) and bill client-favorable, honest fees that reflect any efficiency gained. A Texas Supreme Court Advisory Committee subcommittee has since recommended relying on existing evidentiary and procedural rules rather than adopting AIspecific court rules, so Texas practitioners should expect the general disciplinary framework — not a separate AI rulebook — to keep governing this area for now.

4.3

The broader state bar landscape

AI Legal Research

Texas and the ABA are not outliers — they are part of a wave. As of early 2026, more than 35 state bar associations had issued some form of guidance on generative AI in legal practice. The State Bar of California's Standing Committee on Professional Responsibility and Conduct was among the earliest, issuing practical guidance in November 2023 that addressed bias in AI outputs — a subject the Texas opinion does not directly reach.4 The Florida Bar's Ethics Opinion 24-1 went further than the ABA on client consent, suggesting lawyers should obtain informed consent before using generative AI tools in a matter, not merely disclose their use after the fact.5 New York has likewise issued guidance emphasizing supervisory obligations for firms deploying AI tools across teams of varying experience levels.6

Tex. Comm. on Prof'l Ethics, Op. 705 (2025).

State Bar of Cal., Standing Comm. on Prof'l Responsibility & Conduct, Practical Guidance for the Use of Generative Artificial Intelligence in the Practice of Law (Nov. 16, 2023).

5 Fla. Bar Ethics Op. 24-1 (Jan. 19, 2024).

6 N.Y. State Bar Ass'n, Task Force on Artificial Intelligence, Report and Recommendations (Apr. 6, 2024).

3 4

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4.4

Confidentiality deep dive

Confidentiality deserves particular emphasis because the failure mode is often invisible until it is too late. Many consumer-facing AI tools reserve the right, absent an opt-out or enterprise agreement, to use submitted prompts to further train their models — meaning client facts typed into a free or personal-tier chatbot account could theoretically surface, in some generalized form, in a future response to an entirely different user. Enterprise and legal-specific platforms typically contract around this risk, but the protection is only as good as the specific agreement in place, and terms vary by vendor and by pricing tier within the same vendor. A lawyer's Rule 1.6 obligation requires actually reading and understanding that agreement — not simply assuming that any tool marketed to lawyers is automatically safe for client data. Disclosing information to future users might be the worst-case scenario, but at a minimum, the platform retains the information; employees can access it; you’ve subjected it to data breaches you can’t control; and conceivably, you have disclosed the information to a third party and waived attorney-client privilege.

4.5

Practical takeaway

Treat every AI-assisted research output the way you would treat a firstyear associate's unsupervised first draft: useful, often quite good, but never citable until you have personally verified it against the primary source.

AI Legal Research

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5.

Comparing the AI legal research platforms

AI Legal Research

The market has largely split into two tiers: established legal search tools (Westlaw, Lexis, vLex, Bloomberg Law) that have layered AI assistants on top of decades of curated legal databases, and newer AI-native or workflow-focused tools (Harvey, Clearbrief, Spellbook) that specialize in a narrower slice of the research-and-drafting lifecycle. General-purpose consumer chatbots are included here only as a caution, not a recommendation, for citable legal work.

Platform

Best For

Advantages

Westlaw Precision / CoCounsel

Firms already on Deep integration Westlaw; litigation- with Westlaw's heavy practices primary-law database and KeyCite; agentic "Guided Workflows" for drafting, deposition prep, and contract review; strong brand trust and citation-checking tools

Pricing is not publicly listed and can be costly; independent benchmark testing has found a comparatively higher error rate on complex multijurisdictional queries than some competitors

Lexis+ with Protégé

Firms already on Lexis; citationaccuracy-sensitive research

Full value is realized mainly by firms already paying for the underlying Lexis database; subscription cost scales with firm size

Frequently cited in independent benchmarks as the most accurate of the major legalspecific tools; built-in Shepard's Citations validation; hundreds of prebuilt workflows and a no-code workflow builder

Disadvantages

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One of the largest multi-jurisdictional case-law libraries (over 100 jurisdictions); strong value relative to the Westlaw/Lexis duopoly; now integrated with Clio's practicemanagement ecosystem

Smaller US primary-source depth in some practice areas compared to Westlaw/Lexis; newer integration with Clio still maturing

Harvey AI

Large firms and enterprise legal departments

Highly capable general-purpose legal workflow engine; strong document review, drafting, and research agent chaining; used heavily in BigLaw

Enterprise pricing (often $1,000+ per seat/month with seat minimums) puts it out of reach for solo and smallfirm practitioners

Bloomberg Law

Litigation analytics, dockets, and business-oflaw research

Strong litigation analytics and docket-search tools alongside primary law; useful judge- and expertanalytics features

AI research layer is less central to its value proposition than Westlaw/Lexis; primary strength is analytics, not generative synthesis

Clearbrief

Brief-checking and Built specifically to citation verification check citations, quotations, and record cites in a brief against the underlying sources — directly addresses the

A verification and drafting-support tool rather than a full research platform; typically used alongside, not instead of,

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AI Legal Research

vLex Cross-border, Vincent AI comparative, or (part of Clio) 50-state research

AI Legal Research

hallucination problem

Westlaw/Lexis/vLe x

Spellbook

Contract drafting inside Microsoft Word

Deep Word integration; strong for transactional drafting and redlining rather than case

Not a case-law research tool; limited value for litigation or appellate research

Perplexity

Early-stage issuespotting and orientation on an unfamiliar area of law. Fast summaries of statutes, regulations, or recent developments.

Plain language explanations and general research not going into a filing.

Better than a plain chatbot because it grounds answers in real, fetched web pages rather than inventing citations from memory.

Citations are to the open web, not primary law.

Sources could include a law firm blog, news article, Wikipedia, and other secondary sources. No legalspecific database or jurisdictional awareness. Not appropriate to cite in a filing.

Free / constrained cheap research, students, tools: solo practitioners CasetextDescrybe.ai, generalpurpose chatbots

Low or no cost; useful for initial issue-spotting or informal background reading

Not grounded in a verified legal database; generalpurpose chatbots have shown very high error/hallucination rates on legal queries and must never be cited without independent verification

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5.1

Choosing among them

▪ If your firm already subscribes to Westlaw or Lexis, evaluate that vendor's native AI layer first (CoCounsel or Protégé) — you are already paying for the underlying database, and the integration is typically the most cost-effective.

▪ If your work is genuinely cross-border, multi-jurisdictional, or 50-state, vLex Vincent AI's breadth of coverage is worth a serious look even if it means running two subscriptions.

▪ If your primary concern is citation and quotation accuracy in briefs you are about to file, a dedicated checking tool like Clearbrief is a valuable second layer of defense regardless of which research platform you use for the underlying research.

▪ Reserve general-purpose AI chatbots for background reading, brainstorming search terms, or explaining an unfamiliar area of law in plain language — never for sourcing citations you intend to file.

5.2

Pricing snapshot

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AI Legal Research

Pricing in this market changes quickly and is frequently negotiated or bundled, so treat any figures as directional rather than definitive when a firm budgets for a subscription. As a rough guide reported across recent market surveys: Lexis+ with Protégé has been reported in the range of roughly $150– $250 per user per month depending on firm size and existing Lexis subscriptions; Westlaw Precision with CoCounsel has been reported in the range of roughly $200–$350 per user per month, with some tiers priced separately or left undisclosed; Thomson Reuters' standalone CoCounsel Core plan has been reported at roughly $225 per user per month; Harvey AI has generally required enterprise-scale commitments reported at roughly $1,000 or more per seat per month with seat minimums, putting it out of reach for most small and mid-size firms; and a growing tier of lower-cost entrants has emerged at price points closer to $20–$100 per user per month, generally trading off some database depth or feature breadth for accessibility. Firms should always request current, firmspecific quotes rather than relying on publicly reported ranges.

5.3

Return on investment considerations

AI Legal Research

Evaluating ROI on an AI legal research subscription requires looking past the sticker price. Relevant factors include: the realistic reduction in associate or paralegal hours on routine research tasks; the cost of a verification workflow layered on top of the tool (verification is not free — it takes real attorney time, and that time should be built into any ROI calculation rather than treated as a rounding error); the risk-adjusted cost of a hallucination-driven sanction or malpractice exposure if verification practices are inadequate; and the competitive cost of not adopting AI tools if opposing counsel or competing firms are using them to turn around research-intensive work faster. A firm that adopts a powerful AI research tool without also investing in training and a verification workflow is likely to see a smaller net benefit — and a higher risk profile — than a firm that adopts a more modest tool alongside disciplined verification practices.

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6.

Platform deep dives: what each tool can do

The comparison table in Section 5 gives a high-level view. This section goes deeper on each major platform — what it actually does day to day, and the specific perks that draw firms to it. Pricing and positioning move quickly in this market, so treat the figures here the way you would treat the pricing snapshot above: directional, not final.

6.1

Thomson Reuters CoCounsel (Westlaw Precision)

CoCounsel is Thomson Reuters' flagship AI legal assistant, grounded in the Westlaw case-law database and Practical Law's practitioner-authored guidance.

Originally built on the Casetext CoCounsel product Thomson Reuters acquired in 2023, it was relaunched in August 2025 as "CoCounsel Legal," adding agentic, multi-step capabilities on top of the original single-query assistant, and continues to receive frequent feature updates.

What it can do:

▪ Deep Research: an agentic research capability that plans and executes a multi-step research strategy across Westlaw and Practical Law content, then returns a structured, cited analysis rather than a single-turn answer.

▪ Guided Workflows: prebuilt, multi-step task flows for tasks like drafting a complaint, drafting or responding to discovery requests, reviewing deposition transcripts, and drafting policies, with new workflows added on an ongoing basis.

▪ Document review and drafting: summarizing, comparing, and analyzing uploaded documents (contracts, discovery materials, deposition transcripts), plus Word-native drafting tools.

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AI Legal Research

▪ Knowledge Search: search across both the firm's own uploaded documents and Thomson Reuters' broader knowledge graph from a single interface.

▪ Flexible output formatting: results can be downloaded in multiple file formats and citation styles (including footnotes, endnotes, or none) to match a firm's house style.

Key Perks: ▪ Grounding in a 175-year editorial content base validated by thousands of Thomson Reuters subject-matter experts, which meaningfully reduces (without eliminating) hallucination risk relative to ungrounded tools.

▪ Deepest available integration for firms that already run Westlaw and Practical Law — no separate database subscription required.

▪ Used by more than 20,000 legal teams and reportedly by the U.S. court system itself, which lends it an unusually strong institutional trust signal in litigationheavy practices.

▪ Transparent, step-by-step reasoning display, so the researcher can see what Deep Research searched and why before relying on the output.

6.2

Lexis+ with Protégé (LexisNexis)

Lexis+ with Protégé — renamed from Lexis+ AI on February 24, 2026 — is LexisNexis's equivalent end-to-end research-and-drafting platform, grounded in the LexisNexis content library and Shepard's Citations service. Existing Lexis+ AI subscribers were carried over automatically under the new name.

AI Legal Research

What it can do: ▪ Conversational legal research ("Ask"): plain-English legal questions answered with a detailed, textual response grounded in primary sources, with a list of underlying materials and links provided beneath each answer.

▪ Shepard's-integrated citation validation: citations returned by Protégé — and, through the newer Shepard's Verify feature, citations in any uploaded or AIdrafted document — are checked against LexisNexis's authoritative sources and flagged if they cannot be verified.

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▪

Dual AI configurations: "Protégé Legal AI," tuned specifically for legal accuracy and grounded in LexisNexis content, and "Protégé General AI," which gives secure, in-platform access to general-purpose models from OpenAI, Google, and Anthropic for broader research, drafting, and communication tasks — without leaving the Lexis environment.

▪ Protégé Vault and Protégé Work: a secure document-analysis workspace supporting large volumes of case materials in multiple formats (documents, spreadsheets, audio, video), paired with an agentic feature that routes a described legal task to the right prebuilt or custom workflow.

▪ A library of 300+ prebuilt generative and agentic workflows spanning research, drafting, and analysis, with the ability for firms to build and save their own custom workflows.

Key perks: ▪ Frequently cited in independent, third-party benchmarking (including comparisons reported by Stanford-affiliated researchers) as having a lower citation-error rate than several major competitors.

▪ The ability to toggle between a legal-grounded model and general-purpose frontier models inside one secure workspace, avoiding the need to copy client information into a separate consumer chatbot. Provides confidentiality when using ChatGPT and common chatbot AI models through Lexis integration.

▪ Shepard's Verify Trust Markers extend citation-checking beyond Protégé's own output to any document a user uploads, including a brief drafted by opposing counsel.

6.3

vLex Vincent AI (part of Clio)

Vincent AI is vLex's AI research and workflow assistant, built on top of one of the largest multi-jurisdictional legal content libraries on the market — more Therrian – AI Legal Research - 22

AI Legal Research

▪ Bring-Your-Own-Key encryption option for firms that want direct control over their own data-encryption keys — already deployed at several AmLaw 100 firms.

than a billion documents spanning over 100 countries. Following Clio's roughly $1 billion acquisition of vLex, completed in late 2025, Vincent is increasingly positioned as part of Clio's broader practice-management ecosystem.

What it can do: ▪ Ask a Research Question: converts a natural-language legal question into a fully cited research memo drawn from vLex's primary and secondary source library.

▪ 50-State Survey: compiles a single, structured comparison of the law across all 50 U.S. states (or a subset the user selects) from one query, a task that traditionally consumed days of manual research.

▪ Compare Jurisdictions: a side-by-side breakdown of substantive and procedural differences between selected jurisdictions — U.S. states or countries — including flagging where local counsel expertise may be needed.

▪ Document-driven workflows: uploading a complaint, deposition, or contract to extract claims, facts, and timelines; review redlines and assess negotiation impact; or find related authorities automatically.

▪ Litigation intelligence: judge, opposing-counsel, and party profiling built on more than 850 million court records, plus win-rate and outcome pattern data drawn from similar prior cases.

AI Legal Research

▪ No-code customization through Vincent Studio, allowing firms to build their own repeatable, governed workflows and encode firm-specific analytical preferences as reusable "Skills."

Key perks: ▪ Unmatched breadth for cross-border or multistate work — the native 50-State Survey and Compare Jurisdictions workflows have no close equivalent at Westlaw or Lexis.

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▪ Reported as the most cost-effective of the three major grounded-database platforms on a per-seat basis, making it an attractive entry point for firms not already committed to Westlaw or Lexis.

▪ Deepening integration with Clio's practice-management tools (matter data, document management, billing) now that vLex is part of the Clio ecosystem.

▪ Confidence-scored results — Vincent reportedly ranks retrieved authority by relevance and can be configured to drop low-confidence results below a set threshold, an added layer of self-filtering before an answer reaches the user.

6.4

Harvey AI

Harvey is an enterprise-grade legal AI platform built first for the world's largest law firms and now expanding into large in-house legal departments. It is broader than a pure research tool, spanning research, drafting, bulk document review, and fully custom automated workflows.

What it can do: ▪ Assistant: a chat-based interface for legal research questions, drafting, document analysis, and document Q&A, with citations provided for research answers.

▪ Vault: a bulk document-analysis engine that can run a single query across thousands of documents simultaneously — built originally for M&A due diligence, but also useful for large-scale regulatory or discovery document sets.

▪ Ecosystem integrations: native connections to Microsoft Word, Outlook, SharePoint, and Box, plus a Model Context Protocol (MCP) server that lets external tools query Harvey's research and document capabilities directly.

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▪ Workflow Agents: a no-code builder for custom, multi-step automated processes, with conditional logic, role-based permissions, and the ability to share workflows securely with outside collaborators or clients.

▪ Custom firm-trained models available to the largest AmLaw customers, tuned to a specific firm's house style, precedent library, and institutional knowledge.

Key perks ▪ The most capable single platform on the market for firms with both the budget and the deal/matter volume to make bulk multi-document analysis (Vault) worth the investment.

▪ Deepest workflow-customization tooling of any major platform, letting large legal operations teams encode complex, firm-specific processes without engineering resources.

▪ Enterprise-grade security posture, including SOC 2 Type II and ISO 27001 certification and data-residency options across the US, EU, Switzerland, and Australia.

▪ A dedicated implementation team ("legal engineers") that helps build and tune firm-specific workflows — a high-touch onboarding experience uncommon at this scale in legal tech.

6.5

Bloomberg Law

Bloomberg Law pairs a primary-law research database with a suite of purpose-built AI analytics tools, and is often used alongside — rather than instead of — Westlaw or Lexis, particularly for litigation strategy and transactional benchmarking.

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What it can do: ▪ Brief Analyzer: uploads a brief (yours or opposing counsel's) and automatically checks citations, surfaces the arguments made, and links each point to relevant Bloomberg Law case law and Practical Guidance.

▪ Points of Law: an AI feature that pinpoints the specific passage of a case addressing a particular legal proposition, rather than returning the entire opinion for manual review.

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▪ Litigation Analytics: judge-, court-, and party-level analytics built from millions of docket data points, used to anticipate how a specific judge has ruled on similar motions or arguments in the past.

▪ Draft Analyzer and Clause Advisor: benchmarks contract language against millions of market filings (drawing on SEC EDGAR data) and lets a user adjust clause favorability with an interactive slider, useful for transactional practices that also need research access.

▪ Docket Search and Complaint Summaries: AI classification of federal docket filings by motion type and outcome, plus AI-generated plain-language summaries of case facts and allegations.

Key perks: ▪ The strongest litigation-analytics layer of any platform reviewed here — Bloomberg Law's judge- and court-behavior data is a genuine differentiator for trial and motion strategy, not just legal research.

▪ Bundles business, financial, and regulatory news alongside primary law, which is useful for practices that need to track a client's or opposing party's corporate and financial context alongside the legal issues.

▪ Draft Analyzer's EDGAR-based market benchmarking gives transactional attorneys leverage data (what terms are actually "market") that a pure caselaw research tool does not provide.

6.6

Clearbrief

What it can do:

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AI Legal Research

Clearbrief is not a primary-law research platform — it is a citationverification and brief-drafting tool that installs as a Microsoft Word add-in, built specifically to address the hallucination and unsupported-assertion problem described in Section 3.

▪ Fact-checking against the record: every factual assertion in a brief is checked against the underlying source documents — deposition transcripts, exhibits, medical records, discovery materials — with a hyperlinked citation inserted automatically.

▪ Case-law citation verification: a LexisNexis integration checks legal citations for existence and accuracy, specifically designed to catch the kind of fabricated case citations that have led to sanctions (see Section 3.2).

▪ Unsupported-assertion flagging: identifies statements in a brief that lack adequate citation support, including citations that technically exist but do not actually support the specific claim being made.

▪ Opposing-brief review: a user can run Clearbrief against an opponent's filing to hyperlink all of their citations and quickly check whether the cited authority is accurately represented.

▪ Automated Table of Authorities generation and Bluebook-format citation conversion, saving substantial formatting time on filings with heavy citation loads.

Key perks ▪ Directly targets the single most sanctions-prone failure mode in AI-assisted legal writing — fabricated or misrepresented citations — making it a natural second-layer tool regardless of which research platform a firm uses.

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▪ Works without generative AI for its core verification function, which some litigation teams and courts find reassuring from a reliability standpoint.

▪ Native Microsoft Word integration means no workflow disruption — verification happens in the same document the brief is being drafted in.

▪

Adopted by courts, government agencies, and arbitral bodies (including a partnership with the American Arbitration Association) as well as law firms, reflecting institutional as well as private-practice trust.

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6.7

Spellbook

Spellbook is a Microsoft Word add-in built for transactional lawyers — contract drafting, review, and negotiation — rather than case-law research. It is included in this guide because litigation and appellate practices frequently need contract drafting support alongside research, and because it illustrates how AI tools are increasingly specializing by task rather than trying to do everything.

What it can do: ▪ AI-powered redlining: reads an entire contract and generates clause-level comments and suggested redlines using Word's native Track Changes, so edits appear seamlessly under the reviewing attorney's name.

▪ Playbooks: reusable, saved sets of review instructions that let a firm apply the same negotiating positions and risk tolerances consistently across every contract of a given type.

▪ Smart Clause Drafting: generates new clauses or full agreements from scratch, drawing on the firm's own precedent library or a built-in clause library of common boilerplate language.

▪ Document Q&A: ask questions about the content of an open contract and receive answers with citations to the specific provision being referenced.

▪ Benchmarking: compares negotiated terms against a database of thousands of contract types to flag which provisions are market-standard and which deviate.

Key perks:

▪ Preference-learning redlines that adapt to which side of the transaction the attorney represents, reducing the manual work of tailoring generic AI suggestions to a specific client's interests.

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▪ Purpose-built specifically for contract work, which makes it faster to adopt for transactional practices than a general research-and-drafting platform that treats contracts as one use case among many.

▪ Used by more than 4,500 in-house teams and firms across 80+ countries, giving it one of the larger established user bases among specialized (nondatabase) legal AI tools.

▪ Signed as the exclusive AI contract-drafting and review partner of the Canadian Bar Association in a 2026 deal covering roughly 40,000 members, an unusually strong institutional endorsement for a specialized tool.

6.8

Free and budget tools: a caution

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Free or low-cost tools — Google Scholar's case-law search, generalpurpose chatbots, and emerging budget-priced legal AI products — can be useful for early-stage issue-spotting, background reading, or student use. But the core caution from Section 3 applies with full force: a tool not grounded in a licensed, verified legal database carries materially higher hallucination risk, and none of the sanctions cases cataloged in this guide involved a licensed, databasegrounded research tool used as intended. Budget tools are reasonable as a starting point or a supplement; they are not a substitute for the verification workflow described in Section 8.

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7.

Litigation and appellate research considerations

Appellate and other litigation practices place unusually high demands on citation accuracy, since a brief's persuasive force depends entirely on the record and the case law holding up exactly as represented. A few practice-specific points are worth calling out: ▪ Record citations are not case-law citations, and AI tools grounded in a legal database are not necessarily grounded in your case's trial record. When an AI tool cites to "the record" or a transcript, that citation needs the same independent verification as a case citation — arguably more, since there is no Shepard's or KeyCite equivalent for a trial transcript.

▪ Use AI for issue-spotting and first-pass synthesis across a large case record or a large body of persuasive authority, then hand-verify every citation that will appear in the filed brief.

▪ Run a dedicated citation-check pass (Clearbrief, Shepard's, or KeyCite) as a discrete, documented step before filing — treat it as non-negotiable as a matter of firm policy, not an optional courtesy check.

▪ Where a court's local rules or standing orders require disclosure of AI use in drafting, confirm the specific disclosure format required — these rules vary by judge and district and are still evolving rapidly.

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8.

Best practices and tips for AI legal research

8.1

Prompting and query technique

Be specific about jurisdiction, procedural posture, and the precise legal question — vague prompts produce vague, less-grounded answers. Ask the tool to show its sources and reasoning, not just a conclusion; most legal-specific platforms will surface the underlying cases or statutes it relied on. Break complex, multi-issue questions into separate queries rather than one sprawling prompt — this improves retrieval accuracy and makes verification easier. Ask follow-up questions that probe for counter authority ("What is the strongest case against this position?") rather than only requesting supporting authority — this helps surface the full landscape and reduces one-sided research.

When a citation or holding seems surprising or too convenient, treat that as a specific red flag for closer verification, not a reason for excitement.

8.2

Verification workflow

▪ Pull and read every case an AI tool cites before it goes in a filing — confirm it exists, confirm the quoted language is accurate, and confirm the holding is characterized correctly.

▪ Run every citation through Shepard's or KeyCite (or the platform's built-in equivalent) to confirm it remains good law.

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▪ Cross-check important or close-call research using a second tool or a traditional Boolean search, particularly for dispositive issues.

▪ Keep a simple internal log of which sources were AI-assisted so a supervising attorney can spot-check appropriately — this also supports compliance with supervisory obligations under Rule 5.1/5.3.

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8.3

Confidentiality and data handling

▪ Before inputting client facts, confirm in writing whether the vendor uses customer inputs to train its models, and whether an enterprise or opt-out tier is available that disables this.

▪ Avoid pasting unredacted sensitive client information into general-purpose consumer chatbots that lack an enterprise data-protection agreement.

▪ Maintain a written firm AI-use policy covering which tools are approved, what data may be input, and required verification steps — this is increasingly treated as part of the competence obligation itself, not a mere best practice.

8.4

Workflow integration

▪ Use AI for the first 60–80% of a research task — issue-spotting, initial case pulls, summarization — and reserve attorney judgment for evaluating, narrowing, and verifying the final authority relied upon.

▪ Pair a broad-database platform (Westlaw/Lexis/vLex) with a narrow verification tool (Clearbrief or equivalent) rather than treating either as sufficient alone.

▪ Train new associates and paralegals on both how to use the AI tool and how to independently verify its output — treat AI literacy as a distinct onboarding competency.

▪ Revisit vendor selection periodically; this market is moving quickly; a platform's accuracy, pricing, and feature set can change materially within a single year.

Billing

▪ Disclose AI use in billing practices consistent with Rule 1.5 and Texas Opinion 705 — do not bill flat hourly rates for work substantially accelerated by AI as though it were performed entirely manually.

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8.5

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▪ Consider passing efficiency gains to the client, whether through reduced hours billed, flat-fee arrangements, or transparent disclosure of AI-assisted efficiencies.

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9.

Real-world experiences and reviews

Beyond vendor marketing and formal benchmarks, a growing body of firsthand commentary from working lawyers, law librarians, and legal-tech reviewers — shared through conference panels, professional blogs, YouTube walkthroughs, and online lawyer communities — offers a useful ground-level check on how these tools actually perform day to day. Several consistent themes emerge.

9.1

The law-librarian "smackdown": a structured head-to-head

One of the most-discussed practitioner comparisons in this space came out of a February 2025 meeting of the Southern California Association of Law Libraries, where three working law librarians — Mark Gediman (Alston & Bird), Cindy Guyer (O'Melveny & Myers), and Tanya Livshits (DLA Piper) — ran identical research questions through Lexis+ AI, Westlaw Precision AI, and vLex's Vincent AI and compared the results live for an audience of their peers.7 Their questions covered real practice scenarios: the deadline to seek an interlocutory appeal certification in the Ninth Circuit, whether a private right of action exists under a specific California leave statute, and the standard for appealing class certification.

Bob Ambrogi, In "AI Smackdown," Law Librarians Compare Legal AI Research Platforms, Finding Distinct Strengths and Limitations, LawSites (Feb. 14, 2025), https://www.lawnext.com/2025/02/in-ai-smackdown-law-librarians-compare-legal-airesearch-platforms-finding-distinct-strengths-and-limitations.html.

7

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The panel's findings, later written up by legal-technology journalist Bob Ambrogi, were candid: all three platforms could handle basic research questions competently, but each showed different strengths, and none was reliable enough to skip independent verification. Gediman's summary of the group's takeaway has become a benchmark quote in the legal-tech commentary community: "This is a starting point, not an ending point." Guyer, presenting similar findings at a later law-librarian conference, reported that the comparison surfaced real trust issues whenever a tool returned tangential or off-point results, reinforcing that

experienced researchers still needed to sanity-check AI output against their own knowledge of the law.

9.2

Independent reviewer reactions

Legal-tech commentators who have spent extended hands-on time with individual platforms echo the same cautious-but-optimistic tone. In one widelyread solo-practitioner review of Lexis+ AI written after several months of regular use in an active practice, the author described the tool as deliberately cautious on complex or advanced queries — a design choice they attributed to the vendor's evident awareness of how costly a fabricated citation can be — while also noting the interface still felt less conversational and less able to sustain a back-andforth dialogue than general-purpose chatbots like ChatGPT. Other reviewers comparing CoCounsel and Vincent AI side by side have generally converged on a similar conclusion: the deciding factor for most firms is not raw AI quality but which underlying database (Westlaw, Lexis, or vLex) the firm already pays for, since the AI layer inherits both the strengths and the coverage gaps of its underlying source material.

9.3

Common threads: online lawyer communities and video walkthroughs

AI Legal Research

Across lawyer-run YouTube channels, legal-tech blogs, and discussion threads on forums frequented by practicing attorneys, a fairly consistent, recurring set of practitioner experiences shows up regardless of which specific platform is being discussed: ▪ Time savings are real but overstated by marketing: practitioners commonly report meaningful reductions in first-draft and initial-research time, but many caution that the time saved is partly offset by the added verification work, and that vendor-published productivity statistics should be treated as best-case scenarios rather than typical results.

▪ "Treat it like a junior associate's first draft" is the most frequently repeated piece of advice: reviewers and everyday users alike converge on the idea that AI output should be edited and fact-checked with the same skepticism a supervising attorney would apply to work from a new associate, not accepted at face value because it sounds polished.

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▪ Hallucination anxiety shapes usage more than any other single factor: commentary from both individual practitioners and law-firm management professionals repeatedly cites the fear of an undetected fabricated citation as the top reason for hesitancy, even among lawyers who otherwise find the tools valuable.

▪ Prompting (creating a recurring task akin to computer programming) technique is treated as a learnable skill worth investing in: recurring tips shared in lawyer-focused videos and write-ups include assigning the AI a specific role ("act as a litigator specializing in X"), providing detailed factual context up front the way one would brief a new associate, and explicitly asking the tool to show its reasoning and cite its sources rather than accepting a bare conclusion.

▪ Adoption of AI tools tends to be incremental, not all-at-once: several practitioner accounts describe a deliberate trial period — often framed as a two-to-four-week test — of running an AI tool alongside traditional research methods on real matters before deciding whether and how to integrate it permanently into the practice's workflow.

▪ Solo and small-firm practitioners describe AI as a genuine equalizer: multiple accounts from solo practitioners frame AI-assisted research and drafting as narrowing the resource gap with larger firms that have more associates and paralegals available, while also stressing that without a second attorney or compliance team to catch errors, the individual practitioner bears full responsibility for catching any mistakes the AI makes.

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10. Lexis+ with Protégé tricks This is by no means a sales pitch, but as the paper demonstrates, there are far more AI platforms than a single lawyer could survey in the time they’ve been available on the market. The platform I’ve become most familiar with is Lexis+ with Protégé, for the reasons discussed above in Sections 5.1 and 5.2. I am a partner in a criminal law firm of 11 attorneys, all of whom were already subscribed to the basic Lexis search engine. The Lexis+ with Protégé addition was offset by our already existing Lexis+ subscription. My experience has been one of teaching myself while also trying to teach the AI to do the things I want it to do—an experience that brings us full-circle: it is both brilliant and dumb at the same time. I’ve invested several multi-hour periods with a Protégé window open alongside Claude or ChatGPT, asking the window on the right what to tell the window on the left to make a prompt work better or to learn how to program a workflow. This section, more than anything, is an attempt to spread the costs of labor I’ve invested. Below, you can find some of the neat tricks I’ve been able to do with this specific platform, and how to do them yourselves, should you be so inclined. A quick note about one of the frustrations I’ve encountered: Protégé seems to constantly update its interface and features in a way that makes what you recently learned how to do obsolete—forgive me if the step-by-step instructions become obsolete by the time you read this paper.

10.1 Trick #1—turn a trial transcript into a working case file

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One of the most useful capabilities for criminal litigators is documentbased analysis: upload a transcript, indictment, motion, your discovery, or the full clerk's/reporter's record into a secure Vault, and Protégé will use it as the basis for follow-up questions, drafting, and review rather than working from a blank prompt.

Step by step: ▪ From the Lexis+ with Protégé homepage, select the Documents task (or open Protégé from the left sidebar and choose the document-upload option).

▪ Drag and drop the transcript, indictment, or record into the upload box, or click the browse link to locate the file. Supported formats are PDF, DOC,

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DOCX, and TXT, up to 20 MB per file (larger consolidated records may need to be split or uploaded via Vault's larger-document handling).

▪ If you are uploading more than 10 files at once — for example, a full multivolume reporter's record — the platform will automatically prompt you to create a Vault rather than a one-time session upload.

▪ Name the Vault (e.g., by case number or defendant name) and confirm. A Vault can hold up to 500 documents, and you can create up to 50 Vaults, so a separate Vault per matter works well for a busy criminal docket.

▪ Watch for the green check mark confirming each file uploaded successfully; a red X means the file type or size wasn't accepted and needs to be fixed and reuploaded.

▪ Once the record is in the Vault, select which documents to ground your prompt against — Protégé checks all documents in the Vault by default, and you can uncheck any you don't want included in a given query.

▪ Type a natural-language prompt such as "list every objection and the court's ruling, with page and line citations" or "build a timeline of key events and witnesses from these documents," or use one of Protégé's suggested tasks, which appear automatically based on the document type you uploaded.

▪ Ask follow-up questions in the same session — e.g., "what did the witness say on cross about the timeline" — without re-uploading or re-reading the record yourself.

10.2

Trick #2 — check quotes and cross-check citations before you file

For litigation documents specifically, Protégé Documents can check whether quotations in a draft brief actually match the underlying source (case Therrian – AI Legal Research - 38

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Another platform that does this quite well is Google’s Gemini Notebook (formerly Notebook LM). I’ve used Gemini Notebook to upload multiple volumes of a reporter’s record after reviewing them line by line. Once uploaded to a notebook, Gemini allows you to ask questions such as “what did the victim’s mother say about her outcry?” and Gemini will return a result with a linked record citation.

law or the record), flag unsupported or mischaracterized quotes, and surface similar briefs, pleadings, or motions from LexisNexis's content for comparison.

Every citation that survives into a draft is run through Shepard's, so a case that has been overruled, criticized, or distinguished on the relevant point gets flagged before it becomes a sanctions issue rather than after.

Step by step: ▪ Upload your draft brief or motion the same way as in Trick #1, either as a onetime upload or into the matter's Vault alongside the record.

▪ Because the file is recognized as a litigation document, Protégé will surface litigation-specific tasks — select "check quotes" (or the equivalent task label in your version of the interface) rather than a transactional task like summarize or compare.

▪ Review the output, which flags each quotation and indicates whether it matches the cited source, including the record if the underlying transcript is also in the same Vault.

▪ For each citation in the draft, confirm Shepard's signal indicator is visible and showing positive or neutral treatment; click through to the Shepard's report for any citation flagged with negative or questioned treatment.

▪ Optionally, ask Protégé to "find similar briefs on this issue" to pull comparable filings from LexisNexis's content for a sanity check on how the argument is typically framed and supported.

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▪ Make manual corrections directly in your draft for any flagged quote or citation before the document leaves your desk.

This is particularly valuable in criminal appellate work, where courts and bar disciplinary bodies have become increasingly aggressive about sanctioning fabricated or mischaracterized citations in AI-assisted filings during 2026.

Running a draft through the quote-check and Shepard's validation step before filing is a low-cost, high-value habit.

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10.3 Trick #3 — generate a first draft from the record, not from scratch I consider myself far too much of an artist to use this feature, but I have certainly tinkered with it. Protégé Agentic Drafting is a set of purpose-built drafting agents for motions, briefs, and other litigation documents. Rather than a single generic "write me a brief" prompt, you can direct Protégé to generate a complete first draft — a motion to suppress, a motion for new trial, or an appellate brief section — grounded in the uploaded record and in LexisNexis's authoritative sources, then refine it with integrated editing tools before export to Microsoft Word.

Step by step: ▪ With your case Vault already populated (record, prior filings, relevant exhibits), select the Draft task from the Protégé homepage or type a naturallanguage request such as "draft a motion to suppress based on the attached suppression hearing transcript."

▪ Select the specific document type when prompted (motion, brief section, discovery request, client letter, etc.) — Protégé supports hundreds of pre-built document templates across practice areas.

▪ Specify grounding sources: confirm you want the draft grounded in the Vault documents (the record and any firm templates) in addition to LexisNexis's primary and secondary sources.

▪ Provide any case-specific instructions in the prompt box — jurisdiction, standard of review, the specific point of error, tone, or a firm template/exemplar to follow.

▪ When the draft is ready, export it to Microsoft Word (or another supported format such as PDF) to continue formatting, add signature blocks, and finalize for filing.

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AI Legal Research

▪ Review the generated draft in Protégé's built-in editor. Use the integrated editing tools to revise sections, ask Protégé to regenerate a specific paragraph, or highlight a passage and ask for "similar arguments" — Protégé will offer alternative language you can swap in.

▪ If you work primarily in Word, you can alternatively start in Lexis Create+, LexisNexis's Word-integrated drafting add-in, and pull the same Protégé drafting capability directly into your existing Word document rather than exporting from the web platform.

10.4 Trick #4 — let Protégé work route multi-step tasks for you Protégé Work is the platform's orchestration layer, introduced in the May 2026 expansion. Instead of a single question-and-answer exchange, you describe a broader goal in natural language — for example, "prepare everything I need to respond to the State's brief in this appeal" — and Protégé proposes a structured, multi-step plan for your review before executing it, adjusting as you provide feedback along the way. This is useful for the kind of multi-part criminal appellate workflow (issue-spotting, research, drafting, cite-checking) that otherwise requires juggling several separate tools.

Step by step: ▪ From the Protégé prompt box, either select a pre-built "skill" directly from the skills menu (e.g., complaint analysis, research synthesis, checklist generation) or simply describe your goal in plain language.

▪ Protégé will route the request to the appropriate skill or workflow automatically — you do not need to know in advance which of the 300-plus pre-built workflows applies.

AI Legal Research

▪ Review the structured plan Protégé presents before any work begins. This plan breaks the goal into discrete steps (e.g., "1. Review uploaded record for preserved error; 2. Research controlling authority on [issue]; 3. Draft reply section; 4. Validate citations").

▪ Approve the plan as presented, or edit individual steps — remove a step you don't need, add one you do, or redirect the research focus — before execution starts.

▪ Let Protégé execute the plan, checking in as it produces intermediate work product; you retain visibility and control at each stage rather than receiving a single opaque output.

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▪

Review the final review-ready work product, which can be delivered in Word, Excel, PowerPoint, or PDF depending on the task.

10.5 Trick #5 — cross-check the State's brief for gaps and conflicts Lexis's judicial brief-analysis capability lets a user upload multiple briefs from opposing sides of the same matter and get a comparative report: which authorities each side cited, which authorities neither side cited, and where citations conflict or were mischaracterized. Originally built for judges and clerks, the same tool is available to advocates and is a fast way to pressure-test the State's brief for an omitted controlling case or an overstated holding before you write your reply.

Step by step: ▪ Upload both your own brief (or your client's) and the State's brief — the tool accepts multiple documents per side.

▪ Select the brief-comparison or brief-analysis task rather than a singledocument task.

▪ Review the generated report, which typically breaks down: authorities cited by each side; authorities cited by both; and controlling authority cited by neither side.

▪ Look specifically for the "cited by neither side" category — this is where an omitted controlling case is most likely to surface.

▪ Cross-reference any authority flagged as conflicting or inconsistently characterized against the Shepard's report for that case before relying on the discrepancy in your reply.

10.6 Trick # 6—Finding Obscure or Nuanced Case Law The tricks above assume you already know roughly what you're looking for. Criminal practice often presents the opposite problem: an unusual fact pattern, a narrow procedural wrinkle, or a doctrinal nuance where you don't Therrian – AI Legal Research - 42

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▪ Use the output to prioritize which points to address in your reply brief, focusing your limited page or word count on the State's weakest-supported arguments.

know the term of art courts use for it, and a standard keyword search either returns nothing or drowns you in irrelevant results. Protégé and the underlying Lexis+ platform have several tools built specifically for this situation.

10.7 Trick #7 — guided AI research mode to build the search strategy Rather than typing a single keyword search and hoping it lands, Protégé's guided research mode walks you through a structured research plan built from your fact pattern, so it can surface the correct terminology and controlling authority even when you don't know what to call the issue.

Step by step: ▪ Open Protégé and locate the AI-guided research toggle (usually near the prompt box); slide it on to enter guided mode rather than a single free-text query.

▪ Instead of a keyword search, describe the fact pattern in plain language and as much detail as you have — for example, "my client's cell phone was searched at booking without a warrant and the officer says it was inventory search policy, but the phone was already in a sealed evidence bag." You don't need to know what doctrine governs; describe what happened.

▪ Supply "Prime" context when prompted — jurisdiction, relevant terms of art you do know, and the parties or procedural posture — and "Purpose" context — what you want out of it (e.g., "authority to support a motion to suppress").

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▪ Narrow the source pool to criminal matters only (Protégé lets you limit guided research to civil or criminal sources), which cuts out irrelevant civil-doctrine noise that can otherwise bury an obscure criminal issue.

▪ Review the step-by-step research plan Protégé proposes before it runs — confirm it, discard it and try a different framing, or edit individual steps if the proposed plan is chasing the wrong doctrine.

▪ Use the suggested follow-up prompts Protégé offers based on your fact pattern — these are often phrased using the correct legal terminology for your issue, which you can then adopt as search language elsewhere on the platform.

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▪ Continue with follow-up questions in the same conversation to drill into the specific sub-issue (e.g., "does it matter that the phone was already sealed in an evidence bag before the inventory search") — Protégé retains the context of the conversation for up to 90 days, so you can return to it later without restarting.

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11. The trajectory of AI in legal research Three trends are likely to continue shaping this space. First, consolidation: the market has already seen Casetext absorbed into Thomson Reuters, Leya rebrand into Legora, and vLex acquired by Clio, and further consolidation among smaller entrants is likely as the capital and data requirements to compete rise.

Second, deeper agentic automation (planning and decision-making programmed into the AI tool): platforms are moving from single-query answers toward multistep "guided workflows" that chain research, drafting, and citation-checking together, increasing efficiency but also raising the stakes of any single undetected error propagating through an entire work product. Third, tightening regulatory and judicial scrutiny: with well over a thousand documented sanctions cases worldwide and escalating penalties, courts are moving from case-by-case sanctions toward standing orders and local rules that explicitly govern AI disclosure and certification, and firms should expect this trend to continue.

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A fourth trend deserves separate mention: benchmarking is becoming more rigorous and more public. Early vendor claims about accuracy were largely unverifiable marketing statements; independent academic benchmarks (led by Stanford's RegLab and others, supra) now regularly test major platforms headto-head on standardized legal queries and publish comparative error rates. This is gradually shifting the market toward evidence-based purchasing decisions, and firms evaluating a new platform should ask vendors directly what independent benchmarks, if any, their tool has been tested against — a vendor's unwillingness to point to third-party testing is itself informative.

Finally, expect continued divergence between the "grounded database" tier (Westlaw, Lexis, vLex, Bloomberg Law) and the "workflow layer" tier (Harvey, Clearbrief, Spellbook, and similar tools). Rather than one platform winning outright, the more likely outcome over the next several years is that most firms will run at least two tools in tandem — one for primary-law research grounded in a licensed database, and one or more specialized tools for drafting, citationchecking, or document review.

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Appendix A: glossary of key terms ▪ Hallucination: A confident but false output from an AI model — most dangerously in legal use, a citation to a case, statute, or quotation that does not exist or does not say what the AI claims it says.

▪ Retrieval-Augmented Generation (RAG): An architecture in which an AI model first retrieves relevant documents from a defined database and then generates an answer grounded in those retrieved documents, rather than answering purely from its training data.

▪ Grounding: The extent to which an AI-generated answer is tied to and supported by actual retrieved source documents, as opposed to generated from the model's general training.

▪ Agentic workflow: A multi-step, automated sequence of AI-driven tasks (e.g., research, then drafting, then citation-checking) chained together with limited human intervention between steps.

▪ Shepardizing / KeyCite: The process of checking whether a case remains good law and has not been overruled, criticized, or otherwise limited by subsequent decisions — available as Shepard's Citations on Lexis and KeyCite on Westlaw.

▪

Natural language query: A search phrased in ordinary conversational English rather than structured Boolean search syntax.

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AI Legal Research

▪ Benchmarking: in AI legal research is the practice of testing an AI tool against a standardized, predetermined set of questions or tasks — with known correct answers — to measure how accurate, reliable, or capable it actually is, rather than relying on a vendor's own marketing claims.

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: Developing Your Case Theory Using AI

Speaker:

Grant Scheiner

2211 Norfolk St Ste 735 Houston, TX 77098-4062 713) 783-8998 phone grant@scheinerlaw.com email http://www.scheinerlaw.com/ website

Developing Your Case Theory Using AI

Texas Criminal Defense Lawyers Association

Developing Your Case Theory Using AI

Developing Your Case Theory Using AI

by Brent Mayr

I. Introduction Every trial lawyer has heard the same advice since law school: know your theory before you know your witnesses. A case theory is the single, coherent explanation of what happened and why the client should win. It organizes every exhibit, every cross-examination question, and every line of argument.

Lawyers who skip this step do not try cases; they react to them.

Artificial intelligence does not replace this discipline. It sharpens it. Used well, AI gives the criminal defense lawyer a fast, tireless partner for the two hardest parts of case-theory work: generating more candidate theories than one tired brain can produce alone, and stress-testing each one before trial exposes its weaknesses. Used poorly, AI becomes a shortcut that produces a theory nobody has actually tested — a theory that collapses the first time the State leans on it.

This paper gives a practical, repeatable method for building and pressure-testing a case theory with AI tools. It assumes you already have your facts in hand and your legal research done. The question here is narrower and, in many ways, harder: what do you do with what you know?

II. What a Case Theory Is—and Is Not For decades, trial-advocacy scholars have described a sound case theory using a simple three-part test.

A case theory must be: ●

Legally sufficient. It must, if believed, entitle the client to the verdict you are asking for — an acquittal, a conviction on a lesser-included offense, or an acquittal based on an affirmative defense.

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Factually supportable. It must rest on evidence you can actually get in front of the jury, not on facts you wish existed.

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Psychologically satisfying. It must make sense to a juror using ordinary human experience, not just legal logic.

A case theory is not the same thing as a theme. The theory is the substance: the legal and factual explanation of why the client wins. The theme is the language you use to express that theory to a jury in a memorable way. You can have a correct theory and a forgettable theme, or a catchy theme built on a theory that cannot survive cross-examination. This paper is about building the substance. Turning that substance into an opening or closing that lands with a jury is its own skill, worth a full session on its own.

The distinction matters practically, not just academically, because the two failures look different in the file. A weak theory with a strong theme reads beautifully in a draft opening statement and falls apart

Developing Your Case Theory Using AI

AI A ND T HE AN A T OM Y OF A TR I AL TCDLA Seminar · September 3–4, 2026 · The Woodlands Resort

Developing Your Case Theory Using AI

the moment the prosecutor asks a single hard question on cross. A strong theory with a weak theme survives cross-examination but never quite lands with the jury in argument. AI can help with either problem, but the tools and the prompts differ — this paper addresses the first.

A case theory is also not a defense in the abstract sense. “Reasonable doubt” is not a theory; it is the absence of one. A real theory answers the juror’s unavoidable question: if the client didn’t do what the State says, then what actually happened? Jurors resist a vacuum. A defense that offers no account of events asks jurors to sit with unresolved uncertainty. That is an uncomfortable place to sit, and many jurors will resolve it against the defendant unless the defense fills the gap. Sometimes true reasonable doubt is genuinely the strongest available theory. When it is, AI can still help you find and organize the specific gaps and contradictions that make abstract doubt concrete and visual rather than merely rhetorical.

III. Where AI Fits in the Case-Theory Workflow Case-theory development happens in the middle of the case, after intake and investigation but before you commit to a trial strategy. By the time you sit down to build a theory, you should already have: ●

The offense report, any body-camera or other digital evidence, and everything else produced in discovery

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Legal research on the elements of the charged offense, the available defenses, and any suppression or evidentiary issues

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A basic sense of your client’s account of events

AI adds the most value at three distinct points after that groundwork is done: 1. Generating candidate theories. Given a structured fact set, AI can propose multiple plausible explanations of events far faster than brainstorming alone, including theories a tired lawyer working a heavy docket might not think to consider.

2. Stress-testing each candidate. AI can map facts to legal elements, flag gaps, and, most valuable of all, argue the other side, playing prosecutor against your own theory before the State does.

3. Maintaining consistency. Once you commit to a theory, AI can check every filing, every witness outline, and every proposed exhibit against that theory to catch the contradictions that come from months of case development by different hands.

What AI should not replace is judgment. AI has no stake in the client’s liberty, no feel for a particular jury pool, and no ability to read a witness’s demeanor. Every output described below is a draft for your review, not a conclusion.

IV. Using AI to Generate Multiple Candidate Theories The single biggest risk in case-theory development is “anchoring”: committing to the first plausible story and building the rest of the case around it. Anchoring feels efficient. It is dangerous. A theory adopted in week one and never seriously questioned again is not a strategy. It is a habit.

A Structured Approach Rather than asking an open-ended question like “what’s my defense,” build a structured prompt that gives the tool the raw material to work with: ●

A neutral, chronological statement of the facts as currently known, written by you, not copied from the offense report, so that you control what is fact, what is allegation, and what is disputed

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The elements of each charged offense and any lesser-included offenses

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The elements of any defenses that plausibly apply

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An explicit instruction to generate multiple candidate theories, each supported by specific facts already in the record

Ask the tool to generate several distinct theories rather than one, and to identify, for each, which facts support it and which facts cut against it. This produces a menu, not a verdict. You then apply the judgment a machine cannot: which theory you can actually prove, which your client can credibly testify to, and which fits the courtroom and the panel you expect to draw.

Why Volume Matters A lawyer working alone tends to generate one or two theories and then spend the rest of the case defending the first one. An AI tool, prompted correctly, will generate outlier theories too — including some you will reject immediately. That is fine. The value is not in the outlier theories themselves; it is in the discipline of seeing the full range before you choose. A theory you deliberately rejected after real consideration is a much stronger foundation for trial than a theory you never questioned.

Keep the Client’s Voice Out Until You Choose One caution: do not feed the AI your client’s uncorroborated account as established fact at this stage.

Keep the client’s version in a clearly labeled, separate category from the facts you can independently support. A theory built by blending your client’s account seamlessly with the discovery record will feel solid in your office and fall apart the moment the prosecutor cross-examines your client on a detail no other witness or document can support. Ask the tool to flag, for each candidate theory, which supporting facts come from the client alone versus from an independent source. That single distinction does more to prevent a theory from collapsing at trial than almost anything else in this process.

V. Stress-Testing a Theory Against the Elements and the Evidence Once you have narrowed to one or two candidate theories, the real work begins. A theory that sounds good in a first draft has to survive contact with the State’s case, the rules of evidence, and a skeptical jury.

Developing Your Case Theory Using AI

AI is well suited to breaking anchoring because it has no ego invested in any particular answer. It will generate five theories as readily as one, and it will not get defensive when you ask it to argue against its own suggestion five minutes later.

Developing Your Case Theory Using AI

Element-by-Element Mapping Feed the AI tool the statutory elements of the charged offense alongside your chosen theory, and ask it to test, element by element, whether the theory, if the jury accepts it, actually produces the verdict you want. This sounds mechanical, but it catches real problems. A common failure mode: a theory that persuasively explains why the client lacked a culpable mental state on one element, but says nothing about a second element the State does not even have to work hard to prove. If your theory does not address every element the State must prove, ask the tool to identify exactly which element is left exposed, so you know that going in rather than discovering it during the State’s closing.

Fact-to-Theory Consistency Checks Ask the tool to go through the complete record, not just the facts that support your theory. Then ask it to flag any fact inconsistent with the theory you have chosen. This is uncomfortable but essential.

Lawyers naturally notice the facts that help and skim past the ones that do not. AI has no such bias; it will surface the inconvenient text message, the inconsistent timestamp, or the witness statement that cuts against your account just as readily as the facts that help you. Better to find that fact in your office in July than to have the prosecutor read it to the jury in September.

Adversarial Red-Teaming The single most valuable use of AI in this stage is having it argue against you. Prompt the tool to take the role of the prosecutor and build the strongest available argument against your chosen theory, using only the facts already in the discovery record. Then push further: ask it to identify the three crossexamination questions that would do the most damage to your theory if your client testifies, and the exhibits the State is most likely to use to attack it.

This adversarial exercise does two things a solo brainstorming session cannot. First, it forces you to confront your theory’s weakest points before the State does, while there is still time to either shore them up or abandon the theory for a stronger one. Second, it gives you a rough draft of your own cross-examination preparation, because the questions that would hurt your theory are frequently the same questions you need to be ready to answer on redirect or through other witnesses.

Testing Against the Charging Instrument and Jury Instructions If you have a proposed or pattern jury instruction for your case, feed it to the tool alongside your theory and ask whether the theory maps cleanly onto the instructions the jury will actually receive. A theory that sounds persuasive in the abstract can fail if it does not correspond to how the court will frame the question for the jury. For example, a theory built on the client’s subjective belief fails when the charge asks the jury to apply an objective reasonable-person standard. Catching that mismatch during theory development, rather than after the charge conference, can be the difference between a theory that wins and one that never had a chance.

A Worked Example

Fed a neutral chronology of these facts along with the elements of assault and the elements of selfdefense, an AI tool might generate several candidate theories: straight self-defense, defense of a third party (if a bystander statement suggests the complainant moved toward someone else first), mistaken identity as to who threw the first blow, or a mitigation theory aimed at a lesser-included offense if the client cannot support a full defense.

Element mapping quickly narrows the field. If the security-camera clip shows the client approaching the complainant rather than retreating, a straight self-defense theory built on a reasonable belief that force was immediately necessary under Penal Code § 9.31 may not survive first contact with that clip — not fatal, but a specific problem to solve, perhaps by narrowing the theory to the final exchange of blows rather than the whole encounter. Red-teaming surfaces the prosecutor’s most obvious move: reading the pre-argument text messages to argue the confrontation was mutual and premeditated rather than a sudden response to provocation. That, in turn, tells the lawyer exactly what needs explaining before trial, either through the client’s own testimony or through context supplied by another witness.

None of this removes the lawyer’s judgment. The tool surfaced the security-camera problem and the text-message problem quickly; deciding how to solve them — reframe the theory, find a corroborating witness, or accept the risk and prepare to address it head-on in voir dire and opening — is entirely your call. What the process bought was time: the lawyer found the case theory’s weak points during file review, not during the State’s cross-examination of the client.

VI. Documenting the Theory: The Theory-of-the-Case Memo A case theory that lives only in your head will not survive the months between indictment and trial, and it will not transfer to co-counsel, an associate handling a motion, or a mitigation specialist working the case in parallel. Reduce it to writing early, and keep the document current.

A useful theory memo is short and contains four things: ●

The theory itself, stated in one or two sentences, in plain language a juror could repeat back to you.

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The elements it defeats or mitigates, tied specifically to the charged offense and any lesserincluded offenses.

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The key supporting facts, each with a citation to its source in the record — a page in the offense report, a transcript of a body-worn camera, or a timestamp.

Developing Your Case Theory Using AI

The abstract steps above become clearer with a hypothetical. Assume a client is charged with assault causing bodily injury under Penal Code § 22.01(a)(1) after a confrontation in a bar parking lot. The offense report says the client struck the complainant with a closed fist after a verbal argument; the complainant required stitches. Discovery includes two bystander statements, a partial security-camera clip with no audio, and text messages between the client and the complainant from earlier that evening.

Developing Your Case Theory Using AI

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The known weaknesses, drawn directly from the red-team exercise, along with your current plan for addressing each one.

AI is useful here in a narrower, almost clerical way: once you have written the memo, have the tool check it against the full discovery record and flag any supporting fact that lacks a clear source citation, and any known weakness that appears in the file but is missing from the weaknesses section. This is not theory generation. It is quality control on a document you are about to build an entire trial around, and it catches the kind of gap that is easy to miss after the fortieth read-through.

Update the memo every time a significant new fact surfaces, a motion is won or lost, or a codefendant’s posture changes. A memo that says “self-defense” on the first page of the file and still says “self-defense” unchanged eighteen months later, through a suppression ruling and two continuances, is usually a sign nobody has stress-tested the theory since the day it was written.

Keeping Multiple Hands Consistent In many offices, and in nearly every public defender office, no case is worked by one lawyer alone. One lawyer may draft a motion to suppress while you handle client communication; a mitigation specialist may build a social history while co-counsel preps cross-examination. Every one of those work streams should trace back to the same one- or two-sentence theory statement in the memo described above.

AI’s clerical usefulness returns here: feed the tool the current theory memo alongside a motion draft, a cross outline, or a proposed exhibit list from any member of the team, and ask a single question: does this document support the stated theory, contradict it, or simply have nothing to do with it? A motion drafted by a lawyer who has not seen the latest theory memo is a common and avoidable source of inconsistency — the kind that shows up in front of the jury.

Multiple Counts and Preserved Alternative Theories Many cases do not present a single clean theory. A client facing three counts may need one theory for the charge with strong physical evidence and a different, sometimes inconsistent, theory for a charge that turns on credibility alone. AI can help here by testing theories independently against each charge’s elements rather than forcing one unified narrative where none genuinely fits. It can then flag, for each charge, whether the chosen theories are legally consistent enough to present together to the same jury without confusing the panel or opening a credibility gap the prosecutor can exploit in argument.

The same tension arises with lesser-included offenses. A theory built exclusively around full acquittal can leave nothing on the table if the jury does not fully accept it. Ask the tool to identify, for each candidate theory, what verdict it supports if the jury accepts only part of it — full acquittal, a lesserincluded offense, or nothing at all — so that the decision about whether to request a lesser-included instruction is made deliberately rather than by default.

VII. Guarding Against AI’s Failure Modes in Case-Theory Work AI tools are prone to a few failure modes that matter especially in case-theory work. They are worth naming so you can watch for them.

Confirmation-seeking prompts. If you ask a tool to “help me build the theory that my client acted in self-defense,” you will get exactly that, whether or not the facts actually support it. The tool will find and emphasize supporting facts and quietly underweight the rest. Ask open, comparative questions that generate multiple theories, then stress-test the one you prefer. Avoid closed questions that assume the conclusion.

Over-fitting to available evidence. A tool asked to build the most persuasive possible story from the discovery you feed it will sometimes construct a theory more elegant than the facts actually support, smoothing over gaps a real jury would notice. Ask explicitly for the theory’s weakest links, not just its strengths.

Confidentiality. Case facts, client statements, and discovery material are confidential. Before using any AI platform for this work, know how that platform handles input data and whether it retains or trains on what you submit. This paper does not attempt to resolve the full range of ethical obligations that attach to AI use in criminal defense practice; that ground is getting its own dedicated treatment elsewhere in this seminar.

None of this means avoiding AI. It means treating its output the way you would treat a very fast, very well-read law clerk or first-year associate who has never once set foot in a courtroom: useful for range and speed, never authoritative on judgment.

VIII. Theory Development Beyond the Trial Table Most cases resolve short of a verdict, and a tested case theory earns its keep long before opening statements. A prosecutor evaluating a plea offer is, in effect, evaluating your case theory: how legally sound it is, how well it fits the evidence, and how likely it is to persuade a jury. A theory that has already survived a rigorous red-team exercise is a theory you can present to a prosecutor with genuine confidence, because you already know its weak points and have an answer ready for each one. A theory that has never been tested tends to come across in a plea negotiation exactly as it is — untested — and prosecutors who have tried more than a handful of cases can usually tell the difference and, worse, call you out on it.

The same discipline applies to sentencing and mitigation once a case does resolve short of trial, or after a verdict. A mitigation narrative is not a case theory in the trial sense, but it benefits from the same rigor: specific facts, tied to specific sources, checked for internal consistency, and stress-tested against what the State or the court is likely to emphasize at sentencing.

Developing Your Case Theory Using AI

Fabricated facts. A language model that runs out of real facts to work with will sometimes generate plausible-sounding ones instead. Never let an AI-generated theory summary stand in for the record itself. Every factual assertion in a theory document must trace back to a specific, verifiable source — a line in a video transcript, a paragraph in an offense report — before it goes anywhere near a filing or an outline.

Developing Your Case Theory Using AI

IX. A Practical Workflow: Six Steps From Intake to Trial-Ready Theory 1. Build a clean fact record. Write your own neutral, sourced chronology of the case. Do not skip this step or let the AI build it from raw discovery on the first pass. You need to control what counts as an established fact versus an allegation.

2. Generate candidate theories. Feed the fact record, the elements of the charged offense, and the elements of available defenses into the tool. Ask for several distinct theories, each tied to specific supporting facts.

3. Screen for legal sufficiency. Eliminate any candidate theory that, even if the jury believes every word of it, does not actually produce the verdict you want under the elements and the applicable defenses.

4. Red-team the survivors. For each remaining candidate, have the tool argue the prosecution’s best response and identify the facts that cut against it.

5. Choose and document. Pick the theory that best satisfies the three-part test — legally sufficient, factually supportable, psychologically satisfying — and write it down in one or two sentences. If you cannot state it that briefly, it is not ready.

6. Check every downstream document against it. As you build voir dire questions, crossexamination outlines, and exhibit lists in the weeks that follow, periodically feed the chosen theory back to the tool alongside your latest draft and ask whether the draft still supports the theory or has quietly drifted from it. Case theories decay over the course of trial preparation as new facts surface and new pressures intrude; a periodic consistency check catches drift before it reaches the jury box.

X. Sample Prompt Templates The following templates are starting points. Adjust them to your case’s elements, defenses, and pattern charge language.

Theory generation: “Here is a neutral chronology of facts in a criminal case, followed by the elements of the charged offense and the elements of [defense]. Generate four distinct case theories consistent with these facts.

For each, identify the specific facts that support it and the specific facts that cut against it.”

Element mapping: “Here is my chosen case theory and the statutory elements of the charged offense. For each element, tell me whether my theory, if believed by the jury, defeats that element, and if not, identify what additional fact or argument would be needed.”

Adversarial red-team: “Take the role of the prosecutor. Using only the facts in the attached discovery summary, build the strongest argument against this case theory: [theory]. Identify the three cross-examination questions most likely to damage this theory if the defendant testifies.”

“Here is my case theory: [theory]. Here is a draft [cross-examination outline / exhibit list / voir dire question set]. Identify anything in the draft that is inconsistent with or fails to support the theory.”

Developing Your Case Theory Using AI

Consistency check:

Developing Your Case Theory Using AI

Multi-count testing: “Here are the elements of Counts One through Three and my candidate theory for each. Tell me whether these theories can be presented together to the same jury without internal contradiction, and flag anywhere a juror might reasonably see them as inconsistent.”

These templates work best as a starting draft, not a final prompt. Add the specifics of your case — the exact statutory language, the pattern charge, and the particular posture of the case — before relying on any output. A prompt built from generic language produces a generic answer; a prompt built from your actual file produces something worth your time to review.

XI. Common Pitfalls ●

Treating the AI’s first answer as the theory. The value of the tool is in the range of theories it can generate and the pressure it can apply to each, not in a single confident-sounding output.

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Skipping the red-team step because the theory feels strong. The theories that feel strongest going in are often the ones that have not yet been tested — confidence is not the same as durability.

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Letting the tool blend client statements with independently verifiable facts. Keep the two categories separate throughout and know which category each supporting fact in your theory belongs to.

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Using the theory-development tool for legal research or fact investigation instead of theory work. Those are different disciplines with their own methods and their own risks; treat this as a distinct step that happens after research and investigation are substantially complete, not a substitute for either.

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Forgetting to recheck the theory as the case develops. A theory built in month one on an incomplete record needs to be revisited once discovery is complete, not treated as fixed.

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Building a theory no one on the team can find. A theory that exists only in the lead lawyer’s head, and not in a short-written memo everyone on the file has read, tends to drift silently as different hands build different parts of the case.

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Presenting an untested theory in plea negotiations. A prosecutor can often tell the difference between a theory that has survived internal scrutiny and one that has not; treat the red-team exercise as preparation for that conversation, not just for trial.

XII. Conclusion A case theory is still the single most important piece of intellectual work a defense lawyer does before trial. AI does not change what makes a theory good — legally sufficient, factually supportable, and psychologically satisfying. It changes how fast and how thoroughly you can find that theory and test it before the State does. Used as a brainstorming partner and an adversary in the room, AI makes casetheory development faster and more rigorous. Used as a substitute for judgment, it produces theories that look finished and are not. The discipline belongs to the lawyer. The tool just makes the lawyer faster at applying it.

Appendix: Quick-Reference Checklist Print this page and keep it beside your keyboard during your next case-theory session.

1. Written, neutral, sourced chronology built before any AI prompt — client’s account kept in a separate, clearly labeled category.

2. Multiple candidate theories generated, not just one — each tied to specific supporting facts.

3. Every candidate screened against the elements of the charged offense and any lesser-included offenses.

4. Every surviving candidate red-teamed — AI has argued the prosecutor’s best response.

5. Full fact record checked for anything inconsistent with the chosen theory, not just the facts that help.

6. Theory checked against the proposed or pattern jury charge, not just the statute.

7. One- or two-sentence theory statement written down in a short memo, with sources cited for every supporting fact.

8. Known weaknesses documented in the memo, with a plan for addressing each one.

9. Every downstream document — voir dire questions, cross outlines, exhibit lists — checked against the memo before use.

10. Memo revisited and updated whenever a significant new fact, ruling, or co-defendant development changes the picture.

Brent Mayr is Co-Chair of the TCDLA Ethics Committee and helps staff the TCDLA Ethics Hotline. He is the Managing Shareholder of Mayr Law, P.C. (Houston and The Woodlands), Board Certified in Criminal Law by the Texas Board of Legal Specialization, a former clerk to Judge Barbara Parker Hervey on the Texas Court of Criminal Appeals, and a former Harris County Assistant District Attorney. He is past President of the Harris County Criminal Lawyers Association (HCCLA), a member of the HCCLA Board of Directors, and a member of the faculty at Harvard Law School’s Trial Advocacy Workshop.

Developing Your Case Theory Using AI

Take one case on your current docket and run it through the six-step workflow this week. Not a hard case — a routine one. Build the clean fact record, generate more than one theory, and force the tool to argue against your favorite before you commit to it in writing. The exercise takes less time than a single afternoon in the office, and it will change how you think about the next file that lands on your desk, hard or routine alike.

Texas Criminal Defense Lawyers Association

September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: How To Present Visual Evidence

Speaker:

Clifford Duke

6301 Gaston Ave Ste 1524 Dallas, TX 75214-3983 214.253.4143 phone clifford.duke@michaeldefense.com https://zealousadvocate.com/ website

How to Present Visual Evidence

AI & Anatomy of a Trial

Putting Pictures On The Page How to Present Visual Evidence

How to Use & Object to Visual Evidence in the Age of AI.

Clifford P. W. Duke Senior Trial Attorney Michael & Associates 6301 Gaston Avenue, Suite 1106 Dallas, TX 75214 214-253-4143 Clifford.duke@michaeldefense.com If a picture is worth a thousand words, the video of a deceased victim of a crime testifying from beyond the grave at the sentencing of his murderer multiplies that thousand to the thousandth degree.

Unfathomable, right? But in May of 2025 that’s exactly what happened during a victim impact statement in Arizona. The family of the man murdered in a road rage incident created a five minute video of the victim talking to the court and his killer. Not testimony. Not evidence. At least not yet.

to use it in court any more than you have to be able to personally sequence DNA to cross the State’s expert. An 8x10 glossy and an AI generated recreation both have to meet the same legal standards. You do have to be able to use the legal principles of evidence and admissibility: either to get your stuff in or keep theirs out.

I.

Grounding In The Basics

Visual aids in the courtroom are nothing new.

Witness drawn diagrams, annotated maps, and in court reenactments have foundations in the common law.

Photographs and videos. Diagrams, graphs, and charts. Models. Reenactments. And all of those either physical or now digital.

No matter the medium, the rules of evidence still apply. Admissible evidence, demonstrative admissible evidence, or demonstrative aids still require tests for predicate, relevance, and balancing prejudice. Let’s start by looking at the types of evidence.

A. Types of Evidence

The exponential curve of technology over the last century is astounding. The last decade has been mind boggling. The onset of miniaturization, automation, and AI has only dumped gasoline on an already raging fire.

No one can deny that technology already permeates our jobs as attorneys.

Our inability, or unwillingness, to pull that technology into our courtroom presentations is already bordering on ineffective.

There are three distinct evidentiary categories: (1) admissible evidence considered for all purposes, (2) demonstrative evidence, and (3) demonstrative aids. Each category carries different requirements for admission, different probative weight, and different treatment during deliberations.

The flip side to that coin is that we as lawyers are the gatekeepers to what is coming into the courtroom and how it’s being used.

You don’t have to be a master of technology

Relevant evidence is admissible unless otherwise provided by the Texas Rules of Evidence or other applicable rules. Tex.R.Ev. 402 Evidence is relevant if it has any

1. Admissible for all Purposes

2. Demonstrative Evidence Demonstrative evidence occupies a middle category: it is offered and admitted at trial, but solely for purposes of illustration rather than as a substantive proof of the facts of the case. Id. Demonstrative evidence is admissible in a criminal trial if it tends to resolve some issue in the case and is relevant.

Onwukewe v. State, 186 S.W.3d 81 (2005).

To be admissible, objects offered as demonstrative evidence must meet the tests of relevancy and materiality, as well as the limitations imposed by Tex. R. Evid. 403, Baker at 123. When demonstrative evidence is admitted, the defendant is entitled to a limiting instruction cautioning jurors that the exhibit was admitted solely for demonstrative purposes. Id. 3. Demonstrative Aids Demonstrative aids — also called visual aids or illustrative exhibits — represent the most limited category. While demonstrative aids may be admitted into evidence with a proper predicate, they need not always be admitted into evidence to be shown to the jury. Milton v. State, 572 S.W.3d 234 (2019).

Demonstrations and demonstrative aids do not have independent probative value for determining the substantive issues in the case; instead, they are relevant only because

of the assistance they give to the trier of fact in understanding other real, testimonial, and documentary evidence Id. Because demonstrative aids are not admitted into evidence, the trial court is not required to grant deliberating jurors access to them during deliberations. Fuller v. State, Not Reported in S.W.3d (2000). The decision to allow the use of visual aids, charts, and diagrams during trial is reviewed for an abuse of discretion. Baker at 123.

B. Foundations for Admission (or objection) No matter how fancy the tech is, it still has to be admitted. A 3D holographic rendering and reenactment of the crime scene means nothing if the Court never lets it in.

Even if it looks amazing, the same rules apply.

1.

Relevance

The first question of admissibility is always relevance. “Irrelevant evidence is not admissible.” Tex.R.Ev 402. If you can’t meet the test for relevance it doesn’t matter how shiny your tech is. It’s not coming in.

“Evidence is relevant if: it has any tendency to make a fact more or less probable than it would be without the evidence; and the fact is of consequence in determining the action.” Tex.R. Ev 401. The question is not if a piece of evidence is dispositive of a question in any given case, just that it assists with answering the question. “[E]vidence need not by itself prove or disprove a particular fact to be relevant; it is sufficient if the evidence provides a small nudge toward proving or disproving some fact of consequence.” Douthitt v. State, 127 S.W.3d

How to Present Visual Evidence

tendency to make a fact more or less probable than it would have been without the evidence.

Tex.R.Ev. 401 Evidence admissible for all purposes carries independent probative value on the substantive issues in the case and, once admitted, may be sent to the jury room during deliberations. Baker v. State, 177 S.W.3d 113 (2005)

327, 336 (Tex.Crim. App 2004) citing Montgomery v. State, 810 S.W.2d 372, 376 (Tex.Crim.App.1990).

How to Present Visual Evidence

2. Authentication Once a piece of evidence has been found to be relevant, it still must be authenticated to be admitted to the trier of fact. “Evidence has no relevance if it is not authentically what its proponent claims to be.” Tienda v. State, 358 S.W.3d 633 (Tex.Crim.App. 2012). “To satisfy the requirement of authenticating or identifying an item of evidence, the proponent must produce evidence sufficient to support a finding that the item is what the proponent claims it is” Tex.R.Ev. 901.

It would be easy to get so caught up in advanced nature of AI and Digital evidence to forget that the authentication still comes down to a simple question: is the evidence what it proports to be?

Evidence may be authenticated in a number of ways. Texas Rule of Evidence 901 provides a non-exhaustive list of authenticating evidence through witnesses with knowledge, circumstantial evidence, or comparison with previously authenticated evidence. Texas Rule of Evidence 902 provides a specific list of types of evidence that are self-authenticating and need no further testimony.

Authentication of demonstrative evidence, specifically computer-generated illustrations, must be “what its proponent claims as opposed to any idea of speculation, conjecture, or presumption of what the exhibit represents.” Pugh v. State, 639 S.W.3d 72, 85 (Tex.Crim.App. 2022).

Inaccuracies in exhibits, variations in scale,

and distortions of perspective that may not be exact in every detail do not go to admissibility, only the weight of the evidence. Pugh, 639 S.W.3d at 87 citing Jackson v. State, 477 S.W.2d 879, 880 (Tex.Crim.App. 1972). However the exhibits must still be based in testimonial or evidentiary fact to be admissible. See eg.

Harris v. State, 152 S.W.3d 786, 794 (Tex.App. – Houston[1st Dist] 2004, pet.

Ref’d.) (Video reenactment purporting to show defendant’s viewpoint when she struck complainant inadmissible when expert admitted the defendant’s viewpoint in the car and speed of the car were misrepresented.)

It’s important to remember however that authentication is not the end game on the argument of whether a piece of evidence is what it claims to be. “The ultimate question whether an item of evidence is what its proponent claims then become a question for the fact finder…” Druery v. State, 225 S.W.3d 491, 502 (Tex.Crim.App. 2007). The trial court only needs determine that a jury could reasonably find the evidence authentic within a zone of reasonable disagreement.

Montgomery v. State, 810 S.W.2d 372, 391 (Tex.Crim.App. 1991) (opinion on reh’g).

Even if a piece of demonstrative evidence is found authentic, it goes to weight, not just admissibility.

3. Prejudicial vs. Probative Even if established as relevant and authentic, the other rules of evidence do not go out the door just because they’re technological in nature. Applying Texas Rule of Evidence 403, a court may exclude relevant evidence if its probative value is substantially outweighed by a danger of

unfair prejudice, confusion of the issues, misleading the jury, undue delay, or needlessly presenting cumulative evidence.

Tex.R.Ev. 403.

Potentially inflammatory animations and exhibits can be excluded. See eg. Milton v.

State, 572 S.W.3d 234 (demonstrative video of a lioness trying to eat a human child through glass was improper); Salazar v. State, 90 S.W.3d 330, 338-89 (seventeen-minute memorial video of the victim set to music of Enya and Celine Deion was inadmissible as victim-impact or victim-character evidence).

Don’t drop your 403 analysis just because the technology looks shiny. In fact, make it even harder.

4. Hearsay Remember that demonstrative evidence is not evidence itself. It is interpretations and recharacterization of otherwise admissible evidence to help the finder of fact understand the evidence. “Demonstrative evidence has no independent relevance to the case but is offered to help explain or summarize the witness's testimony or to put events and conditions into a better perspective.”

Harstock v. State, 322 S.W.3d 775, 779 (Tex.App – Fort Wroth 2010 no pet). This is

The problem arises when the facts, documents, or data relied on to create the demonstrative evidence is hearsay. Failure to object to the admissibility of the underlying evidence waives any hearsay objection.

Bermudez v. State, Not Reported in S.W.Rptr (2025) citing Tex.R.App.P. 33.1(a)(1)(A) If you’re going to try and keep out a demonstrative aid, cut out the legs it’s standing on.

5. Constitutional Objections The same analysis applies to constitutional objections. The demonstrative aid or visualization of evidence is based on other admitted or admissible evidence. If that evidence is inadmissible due to issues with confrontation, search and seizure, privilege, or any constitutional or procedural objection you must object to the underlying evidence.

In the same vein make sure you know what evidence is being used to create a demonstrative aid. If it’s based on 99% admissible evidence and 1% inadmissible or objectionable evidence it’s 100% not coming in.

II.

Getting Help With the Advanced

Let’s be honest, who in the world has time for all of this? Navigating security and communication, downloading and reviewing discovery, creating audio visual presentations, and staying on top of the technology curve all while still figuring out how you’re going to even try this case. For the solo attorney it’s near impossible, and it’s not fair. Especially when you consider that your District or County Attorney’s probably has at least one,

How to Present Visual Evidence

The analysis the court uses is no different for technological rather than other demonstrative evidence: (1) the probative value of the evidence; (2) the potential to impress the jury in some irrational, yet indelible way; (3) the time need to develop the evidence; and (4) the proponent’s need for the evidence. Erazo v. State, 144 S.W.3d 487, 489 (Tex.Crim.App. 1991)(en banc)(op. on reh’g).

the reason demonstrative evidence itself is not objectionable as hearsay.

How to Present Visual Evidence

if not multiple, employees doing this type of work full time.

Most of us are aware of Ake v.

Oklahoma and its use to get expert assistance in our cases. We hire DNA experts, firearms experts, child and forensic psychologists.

Why not technological experts to help us distill and prepare technology for our courtroom presentations?

The central premise of Ake was to put a criminal defendant on more equal footing with the State prosecuting them. “[M]ere access to the courthouse doors does not by itself assure” a fair trial. Ake v. Oklahoma, 470 U.S. 68, 77 (1985). Defendants needed “access to the raw materials integral to the building of an effective defense.” Id. “In an adversarial system due process requires at least a reasonably level playing field at trial.”

DeFreece v. State, 848 S.w.2d 150 (TEx. Crim. App. 1993).

The burden will be on us to push the envelope to obtain this help. One of the central analysis points for any Ake motion is that the defendant make a showing of centrality and novelty of an area of the trial.

“The state does not need to ‘purchase for the indigent defendant all the assistance that his wealthier counterpart might buy…” Ehrke v.

State, 459 S.W.3d 606, 615 (Tex. Crim. App. 2015) citing Ake, at 77. “The burden is on the defendant to provide concrete reasons for why the expert should be appointed. Id. citing Ex parte Jimenez, 364 S.W.3d 866, 877-78 (Tex.Crim.App. 2012).

Having an IT Person to review what digital discovery you receive, help you manipulate that digital media for

presentation, and review the State’s digital presentations for accuracy and admissibility is an expert’s job. So make those concrete reasons to your judge. In your motion to the court to show how you need assistance to prepare the presentation of this digital information for the jury. Lay out how you do not have the technical expertise to do so.

Make a record of what the State has available to them and the violation of Due Process, Equal Protection, and Effective Assistance of Counsel when your client is not allowed the same tools. Push the envelope and make sure your client has the same access to the game as those people prosecuting them.

Don’t try to do this on your own.

III.

Conclusion

Technology is amazing. And it can be terrifying. The specific implementation of your use of AI or other computer tools to enhance your courtroom presentation is very personal. Either through your own skill and knowledge or the use of experts and outside assistance, your own creativity and imagination seem to be the limit. But the foundation is the same.

Basic constitutional and evidentiary rules are the bedrock of any evidence going to the trier of fact. Whether preparing your courtroom presentation or reviewing the State’s don’t let bright lights and shiny toys distract from those baseline requirements. Be a lawyer first and keep the technology in its lane.

Texas Criminal Defense Lawyers Association

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Hands On How to Use AI Platforms

Speaker:

Don Flanary

100 NE Loop 410, Ste 650 San Antonio, TX 78216 210.738.8383 phone donflanary@hotmail.com email http://www.flanarylawfirm.com/website

Hands On How to use AI Platforms

Topic:

HANDS ON HOW TO USE AI PLATFORMS A Practitioner’s Guide to the 2026 AI Stack for Criminal Defense

Prepared for the

Hands On How to use AI Platforms

Texas Criminal Defense Lawyers Association “AI & the Anatomy of a Trial” Seminar September 2026

Donald H. Flanary, III Flanary Law Firm, PLLC donflanary@hotmail.com 210-738-8383 100 N.E. Loop 410, Suite 605 San Antonio, Texas

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I. INTRODUCTION A criminal defense practice in 2026 is a data problem. A single federal drug conspiracy indictment can arrive with terabytes of pole-camera video, weeks of intercepted phone calls, thousands of pages of DEA-6 reports, and a witness list that requires background research on twenty cooperators. A State Online Solicitation prosecution can turn on a Cellebrite dump of tens of thousands of chat messages. In each of these cases, the lawyer’s job has not changed: identify what actually happened, find the government’s weak points, and prepare to try the case. What has changed is the volume of raw material that stands between the lawyer and that job.

applications that have matured enough to actually help with that work. It is written for Texas criminal defense lawyers, and the goal is not to persuade the reader that AI will replace lawyers.

It will not. The goal is to give the reader a better understanding of the current landscape, a critical view of what each category of tool is genuinely good at, an honest catalog of the ways these tools fail and a working stack that a solo or small-firm criminal defense practice can adopt today.

II. FOUR HONEST USE-CASES FOR AI IN A CRIMINAL DEFENSE PRACTICE Before surveying the tools, it is worth stating the four use-cases where AI has already earned its keep in this author’s practice. These are not aspirations. They are workflows already running in an active federal and state criminal defense caseload, and each is illustrated in the tool sections that follow.

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This paper is a practitioner’s field guide to the artificial-intelligence platforms, agents, and

A. Reading the record faster than humans can The single largest productivity gain from AI in criminal defense is not drafting. It is reading. Modern language models with million-token context windows can ingest and summarize an entire federal indictment, several thousand pages of discovery, or a full-day’s worth of bodycamera transcripts in a single session and answer specific questions grounded in that material. 1 Document-grounded tools such as Google’s Notebook enforce citation back to the source page, which lowers hallucination risk when the goal is synthesis rather than argument. 2 For a defender,

Hands On How to use AI Platforms

this converts a two-day discovery review into a two-hour session with a follow-up read to verify anything load-bearing.

B. Converting audio and video into searchable text A body-camera file, a jail call, a wiretap intercept, or an interrogation video is functionally unsearchable until it has been transcribed. AI transcription now approaches 96 to 98 percent accuracy on clean audio, and purpose-built defender tools such as JusticeText automate transcription, speaker labels, and clip creation from the exact media types that dominate a criminal caseload.3 The relevant caveat is that AI transcripts are not automatically admissible; they are a research tool that turns hours of media into a searchable corpus for cross-examination and impeachment.4

1MindStudio, Claude’s 1M Token Context Window, https://www.mindstudio.ai/blog/claude-1m-token-context-

window-ai-agents (last visited Aug. 5, 2026).

2Bruce Rosenberg, Document-Grounded AI: A Better Path for Legal Research, 98 Wis. Law. No. 11, https://www.wisbar.org/NewsPublications/WisconsinLawyer/Pages/Article.aspx?Volume=98&Issue=11&ArticleID =31346 (last visited Aug. 5, 2026).

3 TechCrunch, JusticeText Taps AI to Transcribe Evidence for Public Defenders (Sept. 20, 2023), https://techcrunch.com/2023/09/20/justicetext-taps-ai-to-transcribe-evidence-for-public-defenders/ (last visited Aug. 5, 2026).

4Sonix, Court-Admissible Transcription Software, https://sonix.ai/resources/court-admissible-transcription-software/ (last visited Aug. 5, 2026).

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C. First drafts of routine work product AI is a competent first-drafter for structured, formulaic work: motions for continuance, discovery-status letters, standard suppression motions, sentencing memoranda skeletons, cover letters to opposing counsel, and client-update letters. It is not a competent final-drafter for anything with real strategic weight. The workflow that actually works is to have the AI generate a first pass, then edit that pass ruthlessly with the attorney’s voice, verified authority, and the case-specific facts. This paper takes the position that a defender who uses AI to skip the reading is negligent; a defender who uses AI to skip the typing is efficient.

The most under-rated use-case is treating AI as a private, on-call second-chair. Running a suppression theory, a proposed cross-outline, or a plea-agreement clause through two or three different frontier models (Claude, GPT, and Gemini, for example, or Perplexity’s built-in “model council”) catches weak arguments before they leave the office. This paper does not treat AI as an authority. It treats AI as an infinitely patient junior lawyer who reads fast, drafts fast, and never gets offended when the partner rewrites the work.5 III. THE 2026 AI LANDSCAPE The 2026 AI market is confusing on purpose. Vendors overlap. Consumer products masquerade as enterprise platforms. Legal-specific platforms borrow features from generalpurpose assistants. The categories below organizes the landscape by the job the tool actually does, which is the only classification that matters when a lawyer is deciding what to buy.

5MindStudio, The Multi-Model AI Agent Council, https://www.mindstudio.ai/blog/multi-model-ai-agent-council

(last visited Aug. 5, 2026).

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D. A second opinion on legal analysis

A. General-purpose assistants These are the frontier models a lawyer will use for most day-to-day thinking, brainstorming, and drafting work: OpenAI’s ChatGPT (GPT-5 and GPT-5.5, with context windows now reaching one million tokens), 6 Anthropic’s Claude (Opus 4.6/4.8 and Sonnet 4.6, with persistent Projects that hold case files and instructions across every conversation on a matter),7 Google’s Gemini (including Deep Research and NotebookLM), 8 Perplexity (Comet, Deep Research, Labs, Projects, and Perplexity Computer, which runs a coordinated “council” of

Hands On How to use AI Platforms

nineteen underlying models), 9 and xAI’s Grok (which has real-time access to public social-media data but has also drawn regulatory scrutiny for deepfake-imagery misuse).10 Pricing runs from free to roughly $200 per month at the top consumer tier, with enterprise plans above that. For a solo practitioner, one paid seat on Perplexity or ChatGPT and one paid seat on Claude is the practical starting point.

B. Legal-specific research and drafting platforms These are the products marketed directly to lawyers, layered on top of proprietary case-law databases and citators. Thomson Reuters’s Westlaw Precision AI and CoCounsel, and LexisNexis’s Lexis+ AI and Protégé, remain the two dominant legacy platforms, both priced for

6OpenAI, GPT-5, https://openai.com/index/gpt-5/ (last visited Aug. 5, 2026).

7Anthropic, Organize Your Legal Workflows Using Projects, https://claude.com/resources/use-cases/organize-your-

legal-workflows-using-projects (last visited Aug. 5, 2026).

8Google, Gemini Deep Research, https://support.google.com/gemini/answer/15719111?hl=en (last visited Aug. 5, 2026).

9 TechCrunch, Perplexity’s New Computer (Feb. 27, 2026), https://techcrunch.com/2026/02/27/perplexitys-newcomputer-is-another-bet-that-users-need-many-ai-models/ (last visited Aug. 5, 2026).

10 Reuters, EU Opens Investigation into X over Grok’s Sexualised Imagery (Jan. 26, 2026), https://www.reuters.com/world/europe/eu-opens-investigation-into-x-over-groks-sexualised-imagery-lawmakersays-2026-01-26/ (last visited Aug. 5, 2026).

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firms rather than solos, CoCounsel bundles run into the hundreds of dollars per user per month.11 Harvey is a BigLaw-focused platform priced for enterprise buyers, running $500 to $1,500 per user per month.12 The three platforms in this bucket most realistic for a Texas solo criminal defender are Paxton AI (self-serve at $499 per month with a 7-day trial),13 vLex’s Vincent AI (selected by the Minnesota Board of Public Defense for approximately 950 public defenders statewide, which is the largest indigent-defense AI deployment on record), 14 and Descrybe.ai (a low-cost research tool that added its own citator in 2025).15 Midpage is an AI-native legal research plug-in that added full U.S. case-law coverage and a citator in 2025 and is worth watching. 16

This is the smallest and most important bucket for the audience of this paper. TrialKit was purpose-built for criminal defense discovery, handling text, audio, image, and video ingestion in a single searchable case workspace. 17 JusticeText was built specifically for public defenders and automates transcription, speaker labeling, and clip creation for body-camera, interrogation, and jail-call media, the exact evidence stream that dominates a Texas criminal defense caseload. 18

11CostBench, CoCounsel Pricing, https://costbench.com/software/ai-legal-tools/cocounsel/ (last visited Aug. 5,

2026).

12Vaquill, Harvey, Legora & CoCounsel Pricing Reality, https://www.vaquill.ai/blog/harvey-legora-cocounselpricing-reality (last visited Aug. 5, 2026).

13Paxton AI, Pricing, https://www.paxton.ai/pricing (last visited Aug. 5, 2026).

14Clio, Vincent AI Selected by Minnesota Board of Public Defense, https://www.clio.com/enterprise/blog/vincentai-minnesota-public-defenders/ (last visited Aug. 5, 2026).

15LawNext, Descrybe.ai Launches Paid Suite, https://www.lawnext.com/2025/06/free-legal-research-site-descrybeai-launches-a-paid-suite-of-legal-research-tools-including-its-own-citator.html (last visited Aug. 5, 2026).

16Midpage.ai, https://www.midpage.ai/ (last visited Aug. 5, 2026).

17TrialKit Emerges from Stealth with $4.25M in Funding, https://www.trialkit.ai/library/trialkit-emerges-fromstealth-with-4-25m-in-funding-bringing-ai-powered-discovery-for-criminal-defense (last visited Aug. 5, 2026).

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C. Criminal-defense-specific platforms

Both platforms should be evaluated together because they overlap on media handling while diverging on case-management scope; Section V returns to that comparison.

D. Transcription and media platforms This bucket runs from consumer voice recorders to enterprise transcription services. Plaud sells an AI voice recorder with 112-language transcription, speaker labels, and custom vocabulary at consumer prices,19 making it the practical choice for client interviews and dictation. Rev offers both AI-only transcription and human-verified transcription with a claimed 99 percent accuracy,

Hands On How to use AI Platforms

the human tier is the one the defender wants for anything filed, offered as an exhibit, or used for witness impeachment. 20 OpenAI’s Whisper is free and open-source and can be self-hosted for firms that want to keep audio out of vendor clouds entirely. 21 Descript remains the leader in transcript-based video editing (edit the text and the media follows) which is ideal for demonstrative exhibit creation from raw recordings. 22 Real-world accuracy varies dramatically from marketingpage claims: vendor benchmarks of three to eight percent word-error rate on clean audio can degrade to twenty or thirty percent on the compressed, cross-talking, background-noise-riddled audio that dominates actual body-cam and jail-call files.23 E. Document and discovery review platforms Enterprise e-discovery platforms (Relativity aiR, Reveal/Brainspace, Everlaw, and DISCO’s Cecilia) dominate BigLaw civil litigation and are generally priced beyond the reach of a

19Plaud, Plans & Pricing, https://www.plaud.ai/pages/plaud-ai-plan-pricing (last visited Aug. 5, 2026).

20Rev, Pricing, https://www.rev.com/pricing (last visited Aug. 5, 2026).

21OpenAI, Whisper, https://openai.com/index/whisper/ (last visited Aug. 5, 2026).

22

Descript, Pricing, https://www.descript.com/pricing (last visited Aug. 5, 2026).

23novascribe.ai, Most Accurate Transcription Software, https://novascribe.ai/compare/most-accurate-transcription-

software (last visited Aug. 5, 2026).

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solo criminal defense budget. Two options fit small-firm criminal work: Logikcull’s pay-as-yougo pricing at roughly $40 per gigabyte per month with no minimum, and Nextpoint’s per-seat pricing with no data or hosting fees. For most Texas criminal cases, TrialKit remains the default and Logikcull or Nextpoint should be evaluated only for document-heavy (non-media) discovery productions.

F. Voice, video, and image generation and deepfake detection This bucket is where the defender’s risk awareness matters more than the defender’s dayto-day practice. Voice-cloning tools such as ElevenLabs can produce a convincing clone of a real

2026, produces cinematic AI video with synchronized dialogue and sound effects. 25 The detection side of this market (Deepware, Reality Defender, Sensity, Hive, Truepic) offers tools that claim ninety-plus percent accuracy on ideal media, but independent testing collapses those numbers to 56 to 79 percent on real-world compressed video, and human evaluators still outperform automated tools on the hardest cases. 2627 A defender should know these tools exist and should never present any of them to a jury as dispositive. Companion speakers at this seminar address deepfake detection in depth.

24Coval, ElevenLabs Review 2026: Voice Cloning & Synthesis, https://www.coval.ai/blog/elevenlabs-review-2026-

voice-cloning-and-synthesis-capabilities-explained/ (last visited Aug. 5, 2026).

25OpenAI, Sora 2, https://openai.com/index/sora-2/ (last visited Aug. 5, 2026).

26Adaptive Security, How to Evaluate Deepfake Detection Tools, https://www.adaptivesecurity.com/blog/how-toevaluate-deepfake-detection-tools (last visited Aug. 5, 2026).

27Cross-Paradigm Deepfake Detection Systematic Review (arXiv 2026), https://arxiv.org/html/2603.04456v1 (last visited Aug. 5, 2026).

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person’s voice from a sub-minute audio sample.24 OpenAI’s Sora 2, released publicly in September

G. Agents and automation The 2025-2026 story in AI has been the rise of agents, systems that do not just answer questions but take actions across the web and across files. Perplexity Comet is a Chromium-based AI browser that can execute multi-step tasks in-page.28 ChatGPT Agent Mode reasons, browses, fills forms, and connects to email and document repositories. 29 No-code automation platforms such as Zapier, Make, and n8n let a solo firm connect intake, calendar, document, and AI systems without writing code, and n8n specifically can be paired with self-hosted models to keep client

Hands On How to use AI Platforms

data off third-party clouds entirely.3031 For a criminal defense practice, agents are still an emerging category; the most defensible current use is intake automation and standing status-letter workflows, not autonomous work on client matters.

H. Agentic orchestration and multi-model councils The final bucket is the one that ties everything else together. Persistent “projects” or “spaces” in Claude, ChatGPT, and Perplexity let a lawyer scope a workspace to one matter, upload the file, and stop re-explaining the case in every conversation. 32 Multi-model councils, running the same prompt through two or three frontier models and reconciling disagreements, are emerging as a best practice specifically because they catch hallucinations before filing. 33 Perplexity

28HUMAN Security, Perplexity Comet, https://www.humansecurity.com/ai-agent/perplexity-comet/ (last visited

Aug. 5, 2026).

29OpenAI Help Center, ChatGPT Agent, https://help.openai.com/en/articles/11752874-chatgpt-agent (last visited Aug. 5, 2026).

30Zapier, Legal Automation, https://zapier.com/automation/legal-automation (last visited Aug. 5, 2026).

31n8n Community, Building a Self-Hosted Agentic AI Workflow with n8n & Ollama, https://community.n8n.io/t/building-a-self-hosted-agentic-ai-workflow-with-n8n-ollama/286956 (last visited Aug. 5, 2026).

32Perplexity, Spaces Are Now Projects, https://www.perplexity.ai/hub/blog/spaces-are-now-projects (last visited Aug. 5, 2026).

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operationalizes the council pattern natively; a solo can otherwise assemble the same discipline by keeping active tabs open in ChatGPT, Claude, and Gemini and asking each the same question when the answer matters. 34 IV. WHAT I ACTUALLY USE The categories in Section III is descriptive. This section is prescriptive. It walks through the four tools that this author actually uses on active federal and state criminal cases, in the order they enter a matter, with concrete examples of how each earns its cost. This is the “hands-on”

A. Perplexity — the always-on research and drafting partner Perplexity is the tool I reach for first on almost every case. It is best understood not as a chatbot but as an answer engine with inline citations, coupled with an agentic operating layer (Comet, Labs, Projects, Perplexity Computer) that runs a coordinated council of the frontier models (Claude, GPT, Gemini, Grok, and Perplexity’s own Sonar) inside a single interface. A concrete workflow: on receipt of a new federal indictment, the author opens a Perplexity Project scoped to that matter, uploads the indictment and any known discovery, and asks the council to identify the strongest known constitutional and statutory challenges to each count based on Fifth Circuit precedent, with citations. The council output is then treated as a research map, not an answer. Every case cited is verified in Westlaw or Lexis before it appears in any filing. The reason for the model council layer is that hallucination rates drop meaningfully when two independent

34

LawNext, Perplexity Makes Its Move Into Legal (June 2026), https://www.lawnext.com/2026/06/perplexitymakes-its-move-into-legal-unveiling-industry-features-at-computer-for-counsel-event.html (last visited Aug. 5, 2026).

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promise of the paper’s title.

models agree on an authority, disagreement is a red flag, agreement is a starting point for verification.

B. Plaud — AI voice recording and transcription for client and witness work Plaud is a hardware device paired with a mobile app that records, transcribes, and summarizes conversations across 112 languages, with speaker labels and custom vocabulary. The workflow is straightforward: the device rides in a shirt pocket during client meetings, jail visits, and (with appropriate consent) witness interviews. Transcripts are available within minutes and

Hands On How to use AI Platforms

can be piped into the case file. Remember, transcripts are internal work-product, not certified transcripts. They are search-and-review documents, and anything used at trial is either offered through a witness or run through a human-verified transcription service such as Rev. C. TrialKit — the criminal-defense discovery platform TrialKit was purpose-built for criminal defense: it ingests unlimited discovery of mixed types (PDFs, images, audio, video), transcribes media automatically, generates timelines, flags key facts, and supports natural-language search across the entire case corpus. In my practice, TrialKit is the second thing opened after Perplexity on a new federal matter. A concrete example: on a federal drug conspiracy with roughly 400 hours of pole-camera footage, TrialKit’s automated transcription and search reduced the initial “what do we actually have” review from an estimated three weeks of paralegal labor to approximately two days of attorney and paralegal time working the search interface. The platform did not identify the winning cross-examination questions; the lawyer did. The platform made it possible for the lawyer to find the moments where the questions would land.

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D. Descript — the demonstrative exhibit and content editor Descript is not a discovery tool; it is a content tool. Its distinguishing feature is transcriptbased editing: the user edits the text of a transcript and the underlying video or audio edits itself.

In my practice, I use Descript for the creation of demonstrative exhibits and impeachment clips from body-cam or interrogation footage. The transcript is generated in TrialKit or JusticeText, and Descript is used to cut and combine the relevant clips into a demonstrative that can be shown to a jury.

E. How the four fit together

never gets the final citation-verification call. TrialKit handles discovery ingestion and search and never gets the legal-research call. Plaud handles capture and transcription of the lawyer’s own meetings and never appears as an offered exhibit without a human-verified transcript behind it.

Descript handles demonstratives and never touches raw discovery review. This partition matters because it lets the lawyer answer the standing-order question, “did you use AI, and did you verify the output?” with a defensible, tool-by-tool answer.35 V. FOUR TOOLS WORTH ADOPTING NEXT The four tools below fill genuine gaps in the operating stack described in Section IV. They are chosen specifically to complement rather than duplicate Perplexity, Plaud, TrialKit, and Descript. They are ordered by expected return on effort.

35

Gentry Locke, AI Standing Orders Proliferate as Federal Courts Forge Own Paths, https://www.gentrylocke.com/article/ai-standing-orders-proliferate-as-federal-courts-forge-own-paths/ (last visited Aug. 5, 2026).

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I use all of these tools together. Perplexity handles research and drafting orientation and

A. JusticeText — audiovisual evidence for defenders JusticeText is the single most criminal-defense-native product in this document that a Texas defender may not already have. It was built specifically for public defenders, it automates transcription of body-camera, interrogation, and jail-call media with speaker labels and one-click clip creation, and it has been adopted at scale — the Virginia Indigent Defense Commission uses it statewide for public-defender training and case work.36 It functionally overlaps with TrialKit on media handling, so the honest recommendation is to demo both against a live case and choose

Hands On How to use AI Platforms

based on which better fits the mix of media-heavy versus document-heavy discovery in that practice.

B. NotebookLM — document-grounded case-file synthesis Google’s NotebookLM (rebranded Gemini Notebook in mid-2026) is a source-grounded research notebook: the user uploads documents, and the tool answers questions strictly from those documents with citations back to the source page. 37 It is free at the entry tier and bundled into Google AI subscriptions above that. Practical criminal-defense workflow: upload the indictment, offense reports, discovery index, and expert reports into one Notebook and ask it to build a chronology, flag inconsistencies between the offense report and the body-cam transcript, or draft a first-pass cross-outline grounded only in the record. The tool’s Audio Overview feature can compress a fifty-page expert report into a podcast-style summary, useful, but verify anything loadbearing against the source page. 38

36StateScoop, Virginia Public Defenders Use AI to Analyze Body-Cam Video, https://statescoop.com/justicetext-

virginia-public-defenders-body-cam-video/ (last visited Aug. 5, 2026).

37Google, NotebookLM, https://notebooklm.google/ (last visited Aug. 5, 2026).

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C. ClearBrief — citation-verification insurance ClearBrief is a Microsoft Word add-in that verifies whether every citation in a brief actually supports the claim made, hyperlinking the evidence for the entire team. 39 It is not a generative-AI tool, which means it carries none of its own hallucination risk.

D. Claude Projects — a persistent, matter-scoped workspace and 1M-token context Claude is already available inside Perplexity’s council, so this recommendation is not about model access. It is about workflow. A Claude Project is a persistent, matter-specific workspace

the newer Claude Opus 4.6/4.8 and Sonnet 4.6 models support one-million-token context windows with roughly ninety percent retrieval accuracy across the full window. 41 In practice: one Claude Project per active federal case, holding the indictment, discovery index, and defense theory, functioning as a lightweight “second opinion” workspace alongside the Perplexity Project on the same matter. This turns multi-model cross-verification into a repeatable five-minute habit rather than a special occasion.

E. Honorable mention — Paxton AI Paxton AI is the only dedicated legal-research-and-drafting platform in this survey that is priced and packaged for a solo practitioner: $499 per month, self-serve, with a seven-day free

39LawNext, ClearBrief Launches Cite Check Report (Dec. 2025), https://www.lawnext.com/2025/12/clearbrief-

launches-cite-check-report-to-give-law-firm-partners-an-audit-trail-against-ai-hallucinations.html (last visited Aug. 5, 2026).

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that carries custom instructions and uploaded files across every conversation on that case, 40 and

trial.42 It is worth a trial specifically to compare its citation-grounding discipline against Perplexity Deep Research on a real Texas criminal-procedure question before committing budget. It is not on the priority-adoption list because Perplexity plus Claude already covers most of the same ground, but for a practice that wants one dedicated “legal AI” subscription rather than a stack of generalpurpose ones, Paxton is the honest answer.

VI. WHAT’S AHEAD FOR AI

Hands On How to use AI Platforms

A. Agentic legal work OpenAI’s CEO Sam Altman has stated publicly that 2025 saw the arrival of agents that can do real cognitive work, and 2026 will likely see the arrival of systems that can produce novel insights.43 In practical terms, this means the AI-agent-as-junior-lawyer model described in Section II is likely to expand from research and drafting into workflow orchestration: an agent that monitors PACER for new filings on assigned cases, checks in-court deadlines against a firm calendar, generates status letters, and flags upcoming statute-of-limitations events. The practitioner’s duty of supervision under Rules 5.1 and 5.3 does not disappear when the subordinate is a machine.

43OpenCrewAI, Sam Altman’s 2026 AI Agents Roadmap, https://www.opencrewai.com/blog/openai-sam-altman-

2026-ai-agents-roadmap (last visited Aug. 5, 2026).

Page 15 of 18

B. On-device models and the confidentiality frontier Apple’s next-generation Apple Intelligence exposes its on-device Foundation Model directly to developers via a Swift API, including offline operation. 44 Google’s Gemini Nano runs fully on Android with sub-second latency and complete offline privacy. For a defense practice, ondevice models are the clearest path to running AI on highly sensitive client material (jail calls, medical or mental-health records, protected-order pleadings) without any cloud transmission at all.

This is the confidentiality-first workflow of 2027 and it is worth watching closely.

C. Long context and full-trial ingestion

Practically, an entire multi-thousand-page discovery production or a full multi-day trial transcript can be loaded into a single AI session as a routine — not exotic — workflow. Independent testers still find that models lose coherence well before their advertised limit and that accuracy degrades most in the middle of the document, 46 so verification discipline remains essential even at one million tokens.

D. Real-time transcription in courtrooms Verbit’s Legal Capture markets next-generation real-time transcription of legal proceedings, aimed at court reporting agencies and law enforcement. 47 Expect adoption in state

44Apple Newsroom, Next Generation Apple Intelligence (June 2026),

https://www.apple.com/newsroom/2026/06/apple-intelligence-brings-powerful-ai-capabilities-into-everydayexperiences/ (last visited Aug. 5, 2026).

45Framia, GPT-5.5 Context Window Reaches 1M Tokens, https://framia.converge.ai/page/en-US/news/gpt-5-5context-window-1m-tokens (last visited Aug. 5, 2026).

46Groundy, Million-Token Context Window: What Can You Actually Do?, https://groundy.com/articles/milliontoken-context-window-what-can-you-actually/ (last visited Aug. 5, 2026).

47Verbit, Real-Time Transcription for Legal Proceedings, https://verbit.ai/verbit-launches-revolutionary-real-timetranscription-service-for-legal-proceedings/ (last visited Aug. 5, 2026).

Page 16 of 18

Hands On How to use AI Platforms

Claude, Gemini, and GPT-5.5 now support one-million-token context windows.45

and federal courtrooms to accelerate, and expect a live professional-responsibility debate about the role of certified human court reporters.

VI. A PRACTITIONER’S CHECKLIST The checklist below is the operating rule set behind everything above. It fits on a single page and is meant to be printed and posted above the workstation of anyone in the firm who touches AI output.

Hands On How to use AI Platforms

A. Before adopting a tool •

Confirm the vendor’s data-handling practices. No client material is uploaded until this is confirmed in writing.

•

Confirm that the tool does not use client data to train its models by default.

•

Read the vendor’s security and confidentiality documentation. If none exists, the tool does not touch a client matter.

•

Vet the tool against ABA Formal Op. 512 and Texas Op. 705 before adopting.

B. Before filing any AI-assisted work product •

Every case citation is verified in Westlaw or Lexis against the actual case, not a summary.

•

Every quotation is verified against the source page, not against a paraphrase.

•

Every statute is verified against the current code, not a training-data snapshot.

•

The substantive analysis is asked of at least two different frontier models before any decision is made.

•

The brief is run through ClearBrief or an equivalent citation-verification pass before filing.

•

The lawyer of record reads every sentence and takes ownership as the drafter.

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C. Before appearing in a court with an AI standing order •

Familiarize yourself with the standing order.

•

The required certification is prepared and included with the filing.

•

A file record is kept documenting which AI tools were used, on what portions of the filing, and how they were verified.

D. Before offering AI-derived evidence at trial •

The five-gate framework is worked in advance: FRE 901/902 authentication, 401–403

best evidence.

•

Foundation witnesses are identified and prepped to speak to the tool’s reliability and process.

•

An affirmative deepfake-authentication challenge is prepared if the opposing party’s digital evidence is at all suspect, without conjuring doubt where the record does not support it.

VII. CONCLUSION AI does not change what criminal defense lawyers do. It changes how much of the raw

material we can look at, how fast we can build a first draft, and how many different perspectives we can bring to bear on a hard question before we file. That is a real advantage, and Texas criminal defense lawyers who learn to use it will do better work on more cases per year than lawyers who do not. But the advantage runs one way. AI does not verify authority. AI does not develop the theory of the case. AI does not read the jury. AI does not stand up when the judge calls the case.

Every one of those jobs remains the lawyer’s, exactly as it always has been.

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Hands On How to use AI Platforms

relevance and prejudice, 801–807 hearsay, 702 and Daubert reliability, and 1001 et seq.

Texas Criminal Defense Lawyers Association

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: AI with Experts

Speaker:

Eric Devlin

AI with Experts

14027 Memorial Dr # 391 Houston, TX 77079-6826 888,632.6271 phone edevlin@lfg-texas.com email www.lonestarforensicgroup.com/ website

AI for Expert Witnesses If you are reading this article with the desire to ingest a weighty legal treatise, this is not the article that you are looking for. I enjoy reading case law and learned treatises as much as the next lawyer, but this article is more about the uses of technology to enhance your practice.

In my experience, lawyers often make situations more complicated. It is a natural result of our skill set, and in many situations, it can aid us in achieving our goals. The core opinion of this paper will be the opposite of that. It is my opinion that Artificial Intelligence should be used to aid and assist, not to replace.

It is when a person attempts to replace working diligence with a shortcut such as AI that problems begin to crop up. At the end of the day, Artificial Intelligence is only as good as the information put into it.

Artificial Intelligence is a computer system performing tasks normally associated with human intelligence such as learning, reasoning, problem solving, and decision making. This is accomplished in part by providing a knowledge base for the program to make those deductions from. The quality of the output of these tasks is directly

AI with Experts

connected to the quality of input that the intelligence has to work with. Limited input will result in limited output quality.

The best example I can give in the real world is that if a person receives news from a limited source, such as 4chan, then their understanding of reality and the news will be skewed based upon the limited, poor-quality input. This is how we get people storming a pizza place in Washington, DC, to liberate a child sex trafficking ring.

In the AI world, we typically see these issues coming out in “Hallucinations”.

They can result from several issues, but in the legal world, the most common reason is a data gap in the training data, causing the AI to create a reasonable-sounding answer rather than admitting it is unable to provide an accurate one.

This “shortcut” mentality can be seen in a variety of ways. Some of the most prominent examples have been the use of AI to write briefs, including a case out of Georgia, where the state prosecutor used an unidentified AI tool to write a brief in opposition to a motion for new trial, which resulted in the fabrication of two case cites

and the misrepresentation of the actual case findings in two additional cites. The trial court did not catch the fabrications. Still, the appellate court did, resulting in a reversal of the trial court’s order denying the motion for new trial (Payne v. State (Supreme Court of Georgia, May 5, 2026).

This has become such a problematic issue that a British lawyer by the name of Damien Charlotin has developed a database that specifically tracks legal decisions in which generative AI content was found to contain “hallucinated content.” At present, the database has collected 1814 identified case citations in which AI hallucinations were found in legal briefs. The United States cases lead the pack with 1252, with Canada (200), Australia (96), and the United Kingdom (61) in the next positions. The database can be found at https://www.damiencharlotin.com/hallucinations. It is a fascinating rabbit hole to spend some time on.

One of the most interesting factors that I observed through this database and reading the variety of opinions is that the vast majority are actually from Pro-Se litigants. It is a further reminder of the value of having qualified counsel, and that AI is not a lawyer.

Common Ways Digital Forensic Experts Use Artificial Intelligence

the technology to sift through the massive amounts of data that result from examining digital devices. In the more than twenty years that I have been examining digital devices, I have seen the storage drives explode in size.

Early on in my career, on a search warrant, my team located a two-terabyte external hard drive that was the size of a large textbook. At the time, we were shocked at the amount of storage capacity that was present. Now, I carry a small device in my pocket that has eight times the capacity of that “enormous drive.”

The result of this increased storage capacity has been an explosion of items to be stored. Photographs in the millions and Videos that are hours long are commonly located on all kinds of devices. When reviewing photographs, finding millions of images that were downloaded through someone’s travel through a web browser. The

AI with Experts

The use of artificial intelligence in digital forensics mostly comes down to using

images can also appear due to the installation of an application. In one case that I testified in, a detective with only enough forensic training to push a button and generate results made a big issue about hundreds of pictures of various military-style weapons found on a defendant’s device. The prosecutor was attempting to use these images to show the continuing danger of the person and even wanted to introduce them in the case in chief. The plan was to use a simple exported report showing thousands of pictures of these vicious-looking weapons. The AI categorization in the forensic platform had correctly identified these as firearms; the AI categorization software was unable to differentiate between photographs of actual weapons and graphically rendered images of those from a video game. The images that the detective had identified had all been loaded when a game application had been installed. In testimony, I was able to identify the application. It was a popular mobile version of one of the best-selling video games, named Call of Duty. At the time of testimony, it was the number three best-selling application on the App Store. When that application was identified to the jury, many of the young persons on that panel recognized the name; several likely had it installed.

Another frequent use of the AI image categorization is to look for Child Sexual

AI with Experts

Abuse Material or Nudity Images. The image categorization has improved significantly over the years, in large part due to the mass feeding of identified images into the large language models. The results, however, are still far from perfect. Ive seen detectives testify to locating hundreds or thousands of child sexual abuse images based upon the AI categorization. Still, when those images are worked through one by one, it can be embarrassing to the claims when most images are not of a child, or not of a sexual act, or not even of nudity.

The search for images of narcotics is also frequently an area of issue. The AI categorization will tag numerous images, but a closer review will show them to be pictures of grass (frequently mistaken by AI models for marijuana plants), broccoli (frequently mistaken for cut marijuana bulbs), or simply geometrically oval shapes (frequently mistaken for pills).

When examining a teen’s device, it is not uncommon to find several million text messages. The other big change is that those millions of text messages will often be

divided among up to half a dozen or more social communication platforms. Every couple of weeks I discover a new messaging application, and more often than not, it’s a young person’s phone that reveals them.

When reviewing these messages with AI Categorization, the search for language of a sexual or grooming nature is commonly used by law enforcement. This usage, however, still requires careful review. Many slang terms will get past AI categorization models. Those terms that are identified are often part of an innocent conversation where several words or a sentence are taken out of context.

When I am reviewing mass quantities of data, I will use a wide variety of categorization tools to focus my searches on particular databases or a string of messages. I will also use these same categorization tools to look for trends such as who they are communicating with during portions of the day. Are they communicating using standard messaging applications during the day and Snapchat during the night?

One of the words of caution I give when discussing AI tools with lawyers, clients, and forensic examiners is that you need to be careful what type of information has gone into the usage of AI to generate expectations or requirements for a project. I had one client who was sent to me by a lawyer in a civil case. The client was very demanding, which is something we have all worked with at various points in our should be a very simple meeting lasting five minutes for the normal client, but I had allotted fifteen minutes for this one. The client arrived at my office (two hours late, but we will put that to the side) and sat down. I started to explain the procedures and provide him the consent form, but before I could get a full sentence out, he shushed me.

Most of you who know me can imagine my reaction to this in my own lab. The client proceeded to give me detailed, step-by-step instructions on how I was to conduct the forensic extraction and processing, and informed me that he wished to personally inspect each item that I wished to tag and report on before I did so. One of the most entertaining instructions was that I was to conduct a chip-off physical extraction of his Apple iPhone 17. This was an entertaining instruction, because while I am capable of doing chip off (and I very much enjoy doing it because it was an

AI with Experts

careers. The client was to bring his phone to me to complete a forensic extraction. It

expensive skill to acquire and it is very technical), the chips on Apple iPhones (ever since iPhone 5s), are encrypted directly on the hardware, making chip-off results completely encrypted and unable to be decrypted without the built in device hardware keys. The client began to argue with me and even showed me a picture, becoming enraged when I pointed out that the device he was showing was an older model Android device that did not contain whole-chip encryption at the time. When I asked where he had come up with this protocol, he finally admitted that he had ChatGPT write it for him. The client eventually stormed out of my office vowing that he would find someone qualified to perform a correct digital forensic extraction. I was tempted to send him to a couple of my competitors, but managed to ignore that particular devil on my shoulders. The last part of the story is that the device was delivered to me by his lawyer about a month later, with no specific requests about the extraction method.

This issue highlights the problems that arise when relying solely on the guidance of AI when lawyers are preparing a defense. I believe there is value in consulting with a real forensic examiner rather than relying on general AI tools that have significant data gaps.

In following this trail, I spoke with a number of forensic examiner’s about their

AI with Experts

usage of AI tools to aid in their workflow. The examiners that I spoke to about this are all trained in traditional digital forensics, and their experience levels range from five years in the industry to close to thirty years. Each of them spoke about using the traditional tools that are a part of the major forensic platforms that are commonplace in our industry. As described above, these AI platforms are primarily used to help examiners sift through the mass quantities of data recovered from electronic devices.

Other AI tools examiners use can be broken down into categories: tools that aid in reporting and tools that aid in further data sifting.

In the reporting category, several of the examiners I spoke to use common AI programs such as ChatGPT, OpenAI, and Google’s Gemini to aid in the creation of written reports to summarize procedures and results. These can be a helpful way to automate a cumbersome task. I have played with these myself, but I personally chose not to use them to write my reports. When I have played with this, I found no errors in its creation, but my personal choice is based on the way I review and learn. I am a

Kinetic learner. I learn best by doing. The process of writing the report allows me to spend time ingesting the data and looking at it from a detached perspective. While several examiners that I have consulted with use various AI programs to aid them in the writing of reports, more than half chose not to use such tools in this process.

In the data sifting area, most of the examiners that I have spoken to have used a variety of tools. This includes the creation of audio transcription tools for files.

While audio and video data retrieved from electronic devices can be transcribed with tools in our forensic platforms, another common tool used by examiners is Otter.

This program can be found at https://otter.ai.

In the sifting of video data, a tool that I have used with significant success is Icetana. This program can be found at https://www.icetana.ai/en-us. This program can analyze surveillance video. It is on the pricier end of the spectrum. For a more budget-friendly solution, the AI platform that I use is S21 CCTV. This platform can be found at https://www.semantics21.com/digital-forensics-tools/s21-cctv.

In the area of detecting when a report is AI-generated, I would advise caution. I have worked a large number of academic fraud investigations where professors (actually usually just teaching assistants) have declared a student to have cheated due to the submission of papers for classwork grades. These TA’s have submitted the the results at face value. This same software does not take into account the classwork the writings are based on, the assignments dictated, or the topics. The results are an incredibly inaccurate evaluation of the writings and little to no supervision of the assessments. My own child was a part of an assignment in college where the entire class went together and watched a theatrical performance. They then met back with the professor and engaged in a lengthy discussion. For the assignment, the class was given a list of five questions to write a response to. The professor had three teaching assistants. All papers were submitted to the same academic integrity software. Two of the teaching assistants tweaked the parameters of the software to accommodate the assignment. One teaching assistant did not, and loaded all submissions to the software. This third assistant received a report that all students in their section had engaged in cooperative writing and suggested AI as the cause of the cheating. This

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papers to a subscription-based AI software that the university had selected, and taken

teaching assistant gave every student in their section a zero for the assignment because of their cheating. When the students appealed to the professor, the appeal was denied, and the case was referred back to the TA. This is where I became involved. A communication with the Academic Integrity administrator, whom I have gotten to know due to prior investigations, resulted in a more thorough review of the submissions and a reversal of the allegations.

The most accurate free platform that I have found for the detection of AI writing is Originality.ai. This program is https://originality.ai/. In case any of you are curious…this paper received a score of “Likely Original Writing”, but only a

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75%confidence…and I assure you there was no AI in any of this process.

Texas Criminal Defense Lawyers Association

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: How To Tell If It Is Fake

Speaker:

Blakely Mohr

950 E State Highway 114 STE 160 Southlake, TX 76092 817.345.3637 phone blakely@themohrlawfirm.com email

How to tell if its Fake

Technology and Criminal Defense: Promises and Perils VERSION 3

by

BLAKELY I. MOHR

How to tell if its Fake

Managing Partner The Mohr Law Firm, PLLC Fort Worth Southlake San Antonio

AI & ANATOMY OF A TRIAL The Woodlands, Texas September 04, 2026

Technology & Criminal Defense (version 3)

Page 1 of 63

Table of Contents

I. II.

CURRICULUM VITAE OF BLAKELY I. MOHR................................................................................................... 5

INTRODUCTION.............................................................................................................................................................. 6

TABLE A. A BRIEF GLIMPSE OF TECHNOLOGICAL CHANGE ........................................................................... 9

TABLE B. A LONG-TERM TIMELINE OF TECHNOLOGY .................................................................................... 11

III.

DEFINITIONS ........................................................................................................................................................... 12

a.

AI in Law Enforcement ...................................................................................................................................... 22

IV.

EMERGING TECHNOLOGY IN CRIMINAL PROSECUTION ..................................................................... 20

b.

Geofencing Warrant ........................................................................................................................................... 22

c.

Body-Worn Camera Analytics........................................................................................................................ 22

d.

Voice Recognition & Audio Forensics ......................................................................................................... 22

e.

Mobile App & Social Media Surveillance .................................................................................................. 23

f.

Deepfakes & Synthetic Media ......................................................................................................................... 23

g.

Internet of Things (IoT) Evidence ................................................................................................................ 23

h.

Neurotechnology & Cognitive Privacy ....................................................................................................... 23

i.

Real-Time Surveillance & Drones ................................................................................................................ 23

j.

Algorithmic Sentencing & Risk Assessment Tools ............................................................................... 23

k.

Data Aggregation & Digital Footprint Profiling .................................................................................. 24

l. m. n.

Artificial Intelligence (AI) and Machine Learning .............................................................................. 24 Transportation & Robotics ........................................................................................................................ 24

Biotechnology & Sustainability..................................................................................................................... 24

o.

a.

TECH ETHICS.......................................................................................................................................................... 25

Extended Reality (XR) ........................................................................................................................................ 25

V.

SIX TIPS TO SURVIVE ETHICS COMPLAINTS ON TECH & AI ......................................................... 26

b. SBOT Professional Ethics Committee & Texas Disciplinary Rules of Professional Conduct (TDRPC) ........................................................................................................................................................... 26 i.

ii. iii. iv.

April 2025, Ethics Opinion 705 ................................................................................................................ 26

January 2020, Ethics Opinion 686.......................................................................................................... 27 September 2018, Ethics Opinion 680 .................................................................................................... 27 April 2015, Ethics Opinion 648 ................................................................................................................ 28

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How to tell if its Fake

p.

Quantum Computing & Hard Tech .............................................................................................................. 24

v.

TDRPC 1.01......................................................................................................................................................... 28

vi. vii. viii. ix. c. i.

July 2024, ABA Formal Opinion 512: ..................................................................................................... 31 November 2022, ABA Formal Opinion 503 ........................................................................................ 32

March 2021, ABA Formal Opinion 498: ............................................................................................... 32

iv.

September 2018, ABA Formal Opinion 482 ....................................................................................... 33

v.

May 2017, ABA Formal Opinion 477R .................................................................................................. 33

vi. vii. viii.

VI. a. b. c.

VII. a. b. c.

How to tell if its Fake

d. e.

VIII. a.

IX. a. b. c. d.

See also TDRPC 1.14 – Safekeeping Property .............................................................................. 31

American Bar Association Ethics Opinions & Model Rules.......................................................... 31

iii.

d.

See also TDRPC 1.05 – Confidentiality of Information ............................................................ 31

See also TDRPC 5.05 – Unauthorized Practice of Law .................................................................. 31

ii.

ix.

See also TDRPC 1.03 - Communication ................................................................................................ 31

ABA Model Rule 1.1 Comment 8 ............................................................................................................... 34

ABA Model Rule 1.3 – Diligence .......................................................................................................... 34 See also ABA Model Rule 1.4 – Communications ........................................................................ 34

See also ABA Model Rule 1.6 – Client-Lawyer Relationship ....................................................... 34

Malpractice Hard Knocks and Lessons Learned ................................................................................ 34 HARDWARE & SOFTWARE FOR THE TRIAL LAWYER ..................................................................... 40 Tech Considerations.......................................................................................................................................... 40

Hardware Checklist ........................................................................................................................................... 44 Software Checklist .............................................................................................................................................. 44

BUSINESS MANAGEMENT TECH................................................................................................................... 46 MANAGEMENT ...................................................................................................................................................... 46 MARKETING ........................................................................................................................................................... 48 ACCOUNTING ......................................................................................................................................................... 49 FINANCE.................................................................................................................................................................... 50

Human Resources ............................................................................................................................................... 51 INVESTIGATION TECH ...................................................................................................................................... 52

Helpful Investigative Technologies: ......................................................................................................... 52 COURTROOM TECH ............................................................................................................................................ 53 Jury Selection and Algorithmic Guidance .............................................................................................. 53 Evidence and Demonstratives (including Virtual Reality) .......................................................... 53

Remote Testimony & Witness Credibility ............................................................................................. 54 Motions Practice .................................................................................................................................................. 54

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e. f.

Authenticating and challenging digital evidence .............................................................................. 54

g.

Remote testimony & hybrid hearings...................................................................................................... 55

a.

CHAPTER 8 — PERILS OF TECH TODAY ........................................................................................................ 55

XI.

CHAPTER 9 — IS IT FAKE? DETERMINING AUTHENTICITY ............................................................. 59

X.

a.

Demonstratives and VR ................................................................................................................................... 55 Tech Terrors .......................................................................................................................................................... 55

Let’s Play a Game: Is this illegal? ................................................... Error! Bookmark not defined.

Appendix: PRACTICAL CHECKLISTS ............................................................................................................................... 62

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Page 4 of 63

I.

CURRICULUM VITAE OF BLAKELY I. MOHR I was a military brat born on a base in Indianapolis, Indiana. I lived there for a brief period of three days before my father was transferred setting off a series of moves that would expose me to the world.

I would end up living (in order) in Colorado, West Germany, Florida, Oklahoma, California, New Mexico, Missouri, and Texas. I am the oldest of five boys, married the love of my life Samantha, and we presently live in Keller, Texas with our two sons Dylan and Levi and my baby girl Sloane.

LICENSES

My firm has offices in San Antonio, Southlake and Fort Worth, Texas. We mainly practice in the areas of State and Federal Criminal Defense and family law. I created The Mohr Law Firm, PLLC upon the ideals of equality, respect, honor, and pride. I believe that actions speak louder than words and I should treat people the way I want to be treated.

My mother once told me that God gave us two ears and a mouth for a reason…we should listen twice as much as we speak. I try to apply these concepts in my practice to provide better service to our clients.

State Bar of Texas, 2007-present United States Supreme Court

United States Fifth Circuit Court of Appeals United States Western District of Texas

How to tell if its Fake

United States Eastern District of Texas

United States Northern District of Texas EDUCATION

Texas Wesleyan University, Doctor of Jurisprudence/Juris Doctor (J.D.), 2007

University of Texas System, Austin, English Literature/English Creative Writing, 2003 Universiteit van Amsterdam and Tulane University Law School Summer Program International Criminal Law, Death Penalty, & Human Rights

Technology & Criminal Defense (version 3)

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“It took 2.4 million years for our ancestors to control fire and use it for cooking, but 66 years to go from the first flight to humans landing on the moon.”1 II.

INTRODUCTION

1 Max Roser, Programme Director, Oxford Martin School, University of Oxford, World Economic Forum,

https://www.weforum.org/stories/2023/02/this-timeline-charts-the-fast-pace-of-tech-transformationacrosscenturies/#:~:text=The%20pace%20of%20technological%20change,humans%20landing%20on%20the%2 0moon

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How to tell if its Fake

As I write this article, it dawns on me how quickly everything has changed and continues to change. I was born in 1982 before the internet, cell phone, satellite connectivity, and constant contact were the norm. I remember a mountain bike meant freedom and a chance to explore without any supervision. A payphone check-in was the expectation if you couldn’t make it home on time. And if I wanted to learn something new, I would go to the public library to find a new experience in one of the books on the shelves. If I wanted to learn about the past, I would listen to the stories of the old. I used to look at my mom’s Norman Rockwall paintings and feel a longing for a simpler time of days past. Who knew

that twenty years later the 80’s would become known as a simpler time of days past. Or rather, just the past.

Now, in my eighteenth year as a practicing lawyer, I struggle to keep up with the constant change that every day presents. Change is more constant and inescapable. I feel (felt) like I am/was tech savvy. And then a new technology is invented. Some run. Some embrace it. Either way, tech becomes a way of our lives whether we like it or not.

How to tell if its Fake

Today, I ordered and paid for my Starbucks using an App. Amazon delivered same-day purchases to my home. Ring Cameras provided me with a sense of security for my home and offices. I used cellular technology to browse the internet from my Phone. I attended a court hearing using Zoom on my iPad. A translation tool helped me communicate in a language I did not know or understand. My scale reported my body mass index and health. My watch monitored my heart and oxygen levels. My bank and finances updated me electronically through e-mail. I controlled my HVAC system at my house using Nest. I shared my real-time location with my wife using global positioning technology. And when I had writer’s block, I used artificial intelligence prompting to help me organize my thoughts. And when I was bored, I watched satellite news broadcasting a space rocket launching and landing autonomously.

If I was told in the 80’s that all of those technologies would exist in my lifetime, I would have thought you were on the wacky-tobacky and pulling my leg. And if I told you where technology was going next, you would think I was writing a sci-fi novel. The rapid, consequential technological change is affecting our daily lives. From powerful generative AI tools to low-cost commercial drones to VR to digital currency to whatever is next, we need to keep up just to maintain.

And this affects our criminal justice. And we as practitioners of the law have a duty to keep up and to be prepared for whatever is next. Our courts have gone virtual, spurred by the COVID era. ChatGPT has become part of appellate law, good or bad. A juror is constantly in contact with information at his or her fingertips. Social media has influenced ideas, relationships, and politics.

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Ironically, the same technologies that have group sourced knowledge have also simultaneously pushed people’s confirmation bias into overdrive.

Technology offers real advantages to criminal investigations and criminal justice, while also creating new dangers to fairness and accuracy. This article surveys the biggest developments in technology, explains how to implement tech in courtroom practice (jury selection, motions, evidence presentation), describes tools that improve investigations and firm operations (management, marketing, accounting, HR), and warns about the specific dangers — especially synthetic audio/video (“deepfakes”) and opaque algorithmic decision-making — that every Texas criminal defense lawyer should recognize and be prepared to meet.

Technology improves access to justice, speeds investigations, and creates persuasive demonstratives that can help juries understand complex facts. But the same advances create new modes of deception — from subtle data-biases to wholly fabricated audio/video. The dichotomy of technology shall always challenge the foundational safeguards of reliability and fairness. For Texas criminal defense lawyers, the work now is twofold: use tools that make better defense possible and push back where technology risks producing wrongful convictions.

If there’s one practical takeaway: treat every piece of digital media and every algorithmic output as testable evidence — demand provenance, testability, and explanation — and make judges and juries understand both the power and the limits of modern technology.

From the printing press to the Industrial Revolution to the microchip to artificial intelligence, change continues to happen. Rapidly.

History provides a glimpse of this rapid change:

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How to tell if its Fake

My hope is that you will be more informed in areas of tech that you can immediately implement in your criminal defense practice. Implementing any tech can be incredibly expensive and time-consuming. Hopefully, my paper can provide you with affordable alternatives that you can implement now and save you some time on the learning curve.

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TABLE A. A BRIEF GLIMPSE OF TECHNOLOGICAL CHANGE2 4000– 3400 BC Oldest Evidence of Wheels found in Ukraine, Poland, and Germany th 5 Century BC Catapult in Magadha, India; Cast Iron in Ancient China; Wheelbarrow Ancient Greece. th 4 Century BC Gears in Ancient China th 4 Century BC Cheque System in Maurya Empire, India th 4 Century BC Invention of concept of Zero in Babylon th 4 Century BC Reed Pens, used to write in ink on Papyrus Egypt 3rd Century BC Analog Computers in the Hellenistic World, the Antikythera Mechanism, Rhodes, Ancient Greek; Archimedes Screw, an early hydraulic machine, by Ancient Egypt st 1 Century BC News Bulletin by Julias Ceaser 37 BC – 14 BC Glass Blowing in Jerusalem 100-200 AD First use of Respiratory Protective Equipment by Roman Empire 132 AD Seismometer and Pendulum in Han Dynasty, China 200-400 AD Water Turbine by Roman Empire, Tunisia 300-400 AD Fishing reel in Ancient China 589 AD Toilet Paper in Sui Dynasty China 600-700 AD Tang Dynasty China invents the banknote to avoid the heavy bulk of copper coinage in large commercial transactions 974 AD Fountain Pen invented in Arab Egypt 1956 AD IBM invented the first hard disk drive 1957AD IBM invented the first personal computer used by a single person and controlled by a keyboard, IBM 610, 1998 AD Google Search Engine is launched.

1999 AD Capsule Endoscopy performed for first time.

2001 AD FDA approves first contraceptive patch.

2003 AD Android Operating System developed for mobile devices.

2004 AD First Podcast was created by Adam Curry and David Wine.

2005 AD YouTube was founded by Jawed Karim.

2006 AD Amazon pioneer Cloud Computing 2007 AD Kindle E-Reader introduced by Amazon.

2007 AD iPhone was released by Apple for the first time.

2008 AD First Blockchain created by Satoshi Nakamoto 2 Timeline of Historic Inventions, Wikipedia the Free Encyclopedia, Timeline of historic inventions -

Wikipedia

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2010 AD 2011 AD 2014 AD 2014 AD 2015 AD 2016 AD 2016 AD 2019 AD 2022 AD

Solid-State LIDAR introduced replacing mechanical scanning with electronic beam steering.

Apple introduces Siri as first major company’s virtual assistant Pepper introduced as first commercial humanoid robot Amazon introduces smart home technology through Echo and Alexa Discovery of CRISPR gene editing Method Artificial Pancreas introduced for commercial purposes.

SpaceX launches first successful multi-use rocket.

IBM launches IBM Q System One, first integrated quantum computing system for commercial use.

ChatGPT launched for public use as first mainstream generative AI.

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TABLE B. A LONG-TERM TIMELINE OF TECHNOLOGY

3 3 Max Roser (2023) - “Technology over the long run: zoom out to see how dramatically the world can change

within a lifetime” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/technology-long-run' [Online Resource]

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“Never regret thy fall, O Icarus of the fearless flight For the greatest tragedy of them all Is never to fell the burning light.”

- Oscar Wilde

III. DEFINITIONS To implement technology in your practice, you must first understand technology. Just like installing any hardware in your home or office, there is always an instruction manual. If you don’t understand what you are installing, how will you maintain it? How will you repair it? All good manuals start with warnings and definitions.4 Let’s get learning.

Access to Digital Court Filing Systems: Platforms like PACER (federal) or state-specific e-filing portals for submitting motions and pleadings electronically.

Algorithmic Bias: When AI systems used in policing/sentencing reflect racial, gender, or socio-economic bias.

Application Programming Interface (API): A set of rules

4 ChatGPT.com search query “Provide a list of definitions on key technologies that a lawyer must

understand/own who practices criminal defense”.

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Agile: A software development methodology focused on iterative, incremental, and adaptive development.

It emphasizes collaboration and rapid delivery of functional software.

Algorithm: A set of instructions or rules designed to solve a problem or perform a calculation.

Algorithms are the basis for all software and computer operations.

that enables different software entities to communicate with each other. APIs are crucial for building complex applications and services.

Artificial Intelligence (AI): The ability of a computer system to perform tasks that typically require human intelligence, such as problem-solving, learning, and decision-making.

Augmented Reality (AR): Enhancing the real world with computer-generated information.

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Automated License Plate Readers (ALPRs): Cameras that scan and record vehicle license plates, logging location, and time — used to track vehicle movements over time.

Back End: The part of a website or application that is not visible to the user. It includes the server, database, and business logic that powers the application.

Big Data: Large data sets analyzed to find patterns and trends.

Blockchain: A decentralized digital ledger used to record

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transactions across multiple computers. Technology behind cryptocurrencies; increasingly relevant in financial crimes.

Body-Worn Cameras (BWCs): Cameras worn by police officers to record interactions — frequently used as evidence in use-of-force or arrest-related cases.

Cameras capture audio/video and are key for discovery and impeachment.

Call Detail Records (CDRs): Logs maintained by telecom providers showing incoming/outgoing calls, text messages, duration, and location.

Can be used to establish timelines and contacts.

Cell Site Location Information (CSLI): Data from cell towers that shows which tower a phone connected to at a given time. Can be used to approximate a person’s location. Data shows which cell tower a phone connected to track location on certain dates and times.

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and admissibility in court. The documented process of preserving and tracking electronic evidence integrity.

Data Analytics: The process of examining data to draw conclusions, often with specialized software.

Cloud Backup Service: Services like Backblaze or Carbonite that provide continuous cloud backup of case files, ensuring you never lose critical documents. Data stored remotely (Google Drive, iCloud, Dropbox) is subject to warrants and subpoenas.

Database: An organized electronic collection of data that can be efficiently accessed and managed.

Client Portal: A secure, clientfacing online platform where clients can upload documents, check case status, and message the attorney securely.

Cybersecurity: Protecting computer systems, networks, and data from digital attacks and unauthorized access.

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Dedicated Work Computer with Encryption: A secure, encrypted laptop or desktop used solely for legal work, with fulldisk encryption to protect all stored data in case of theft or loss.

Deepfakes: AI-generated fake videos or audio; increasingly an issue for evidence authenticity.

Digital Calendar & Deadline Tracker: Integrated calendars (Google Calendar, Outlook) with court deadline features and reminders to avoid missed filings or appearances.

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Cloud Computing: A model for delivering on-demand computing services—including storage, servers, databases, and software—over the internet ("the cloud"). This allows for greater flexibility and efficiency than running applications on a local server or computer.

Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Digital Evidence Review Software: Tools for reviewing video, audio, and digital images (e.g., iNPUT-ACE, CaseGuard Studio), helpful for analyzing police bodycam or surveillance footage.

Digital Forensics: Process of identifying, preserving, analyzing, and presenting digital evidence from devices like computers, smartphones, and servers.

Digital Jury Selection Tools: Software that aggregates juror data for voir dire strategies.

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Digital Tracking Devices: Devices like GPS trackers or Bluetooth tags used for surveillance or stalking — their use and legality often play a role in criminal cases.

Document Automation Tools: Software that auto-generates standard legal documents using templates (e.g., LawYaw, Woodpecker), saving time and minimizing errors.

Drones & aerial imaging: Affordable unmanned aircraft and publicly available aerial

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imagery are increasingly used in investigations, scene documentation, and impeachment.

Edge Computing: A distributed computing approach that brings computation closer to the data source.

eDiscovery Tools: Applications like Logikcull or Nextpoint that allow for the collection, review, and production of electronically stored information (ESI).

E-Discovery: The process of identifying and producing electronically stored information (ESI) during discovery — important in digitalheavy cases.

E-Filing / E-Court Systems: Digital record systems attorneys must navigate.

Electronic Monitoring (e.g., Ankle Bracelets): GPS-enabled devices used for tracking individuals on probation, parole, or pretrial release — understanding their operation is crucial for defending violations.

Encrypted Email Service: A secure email platform (e.g., Page 15 of 63

ProtonMail, Tutanota) that encrypts messages to protect sensitive client communications from interception.

Encrypted Messaging Apps: Apps like Signal, WhatsApp, or Telegram that encrypt communications — important when evaluating client communication and potential evidence, especially for clients without access to traditional email.

Encryption: Technology protecting data (end-to-end encryption in messaging apps).

External Hard Drive with Backup Software: Used for regular, offline backups of case files. Essential for protecting data in the event of a cloud failure or ransomware attack.

Forensic Imaging / Bit-by-Bit Copy: Exact duplication of digital storage devices for forensic analysis.

Forensic Video Analysis: The process of enhancing or interpreting surveillance or dashcam footage — often critical in disputed encounters.

Front End: The part of a website or application that the user interacts with directly. This includes the graphical user interface, text, and design.

Generative AI (GAI): Systems that create text, images, audio, or code in response to prompts.

They accelerate drafting and review but require human supervision for accuracy and confidentiality.

Tech like ChatGPT that produces text/images, relevant for fabricated evidence or threats.

Firewall: A network security system that controls network

Geofence Warrants: Broad warrants requesting Google/Apple location data for all devices near a crime scene.

Warrants that request data from

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Facial Recognition Technology (FRT): Software that identifies individuals by analyzing facial features, often used by law enforcement in investigations.

AI-driven software identifying suspects from images or video.

traffic to prevent unauthorized access.

tech companies (e.g., Google) about all devices in a geographic area during a specific time window — raising 4th Amendment issues.

Geolocation Data: Data that shows the physical location of a device (typically from cell towers, GPS, or Wi-Fi networks), often used to place a person at a scene.

Hash Value: A digital fingerprint used to verify that evidence hasn’t been altered.

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Internet of Things (IoT): A network of physical devices embedded with sensors, software, and other technology.

These devices collect and exchange data with other devices and systems over the internet.

IP Address: Digital identifier for devices on the internet, used in cyber investigations.

Legal Case Management Software: Platforms like Clio, MyCase, or PracticePanther that help organize case files, track court dates, manage contacts, and log time/billing.

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Legal Research Platform Subscription: Access to services like Westlaw, LexisNexis, or Fastcase for finding relevant case law, statutes, and legal commentary.

Malware: Malicious software designed to damage computer systems or networks.

Metadata: Hidden data embedded in digital files (like documents, images, or emails) that provides information about the file’s creation, modification, and usage. Data about data (timestamps, geolocation, file creation info) is often critical in digital evidence.

Mobile Device Forensics: The recovery and analysis of data from smartphones and tablets, including texts, call logs, app data, and deleted information.

Operating System (OS): The fundamental software that manages a computer's hardware and software resources. It provides a platform for running applications and performing basic functions.

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Password Manager: A tool (e.g., LastPass, 1Password) that stores and auto-generates strong, unique passwords for different accounts, reducing the risk of breaches.

PDF Editing Software: Software like Adobe Acrobat Pro for organizing, annotating, redacting, and submitting court documents in proper format.

Phishing: A cyberattack where scammers use fraudulent messages to trick users into revealing sensitive information.

Predictive Policing: The use of algorithms to analyze data and predict where crimes are likely to occur or who may be involved — a controversial tool with bias concerns. AI-based systems predict where crimes are likely to occur.

Quantum Computing: A type of computing using quantum Technology & Criminal Defense (version 3)

Remote and hybrid courts: Videoconference hearings, remote testimony, and asynchronous evidence sharing are now routine in many Texas courts.

Scanner with OCR (Optical Character Recognition): A document scanner that converts printed documents into searchable digital files — crucial for digitizing discovery and case notes.

Secure File-Sharing Platform: Cloud-based tools (e.g., Sync.com, Tresorit, or encrypted Dropbox alternatives) that allow sharing of case files with clients and experts securely.

Self-authentication of digital evidence: Rules allow certain electronic records and forensic copies to be authenticated by certification rather than live testimony (e.g., “902(13)” and “902(14)” analogs).

ShotSpotter: A gunshot detection system that uses microphones placed around a Page 18 of 63

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Presentation Software for Trial: Tools like TrialDirector or PowerPoint adapted for legal settings — used to display exhibits, timelines, and evidence in court.

mechanics to solve complex problems.

city to detect and locate gunfire — often challenged for accuracy in court.

Software-as-a-Service (SaaS): A software licensing and delivery model in which software is centrally hosted and licensed on a subscription basis. Users can access it over the internet.

Spoofing / Phishing: Techniques used in cybercrime involving identity disguise or fraudulent emails.

Stingray / IMSI Catcher: Devices that mimic cell towers to intercept phone traffic.

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Synthetic media / deepfakes: AI-generated or manipulated audio/video/images that convincingly depict events that never occurred. Treat such media as testable evidence and demand provenance.

Tor Network / Dark Web: Tools and platforms that anonymize internet activity — relevant in cases involving illicit online

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marketplaces or communications.

anonymous

Trial Presentation Software: Tools like TrialDirector, TrialPad or PowerPoint for visual advocacy.

Virtual Hearings / Remote Testimony: Technology enabling Zoom or video-based proceedings.

Virtual Private Network (VPN): A technology that encrypts internet traffic and masks a user's IP address, often relevant in cybercrime or privacy-related defenses. It protects online activity, especially when accessing case files remotely.

Virtual Reality (VR): A simulated, computer-generated environment that users can explore and interact with using equipment like a headset.

VoIP Phone System: Systems like RingCentral or Google Voice that allow call logging, voicemail transcription, and business number masking on mobile.

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IV.

EMERGING TECHNOLOGY IN CRIMINAL PROSECUTION

Unfortunately, the scary definitions section above only discusses what is historically known in technology and not what is to come. Emerging Technologies create significant social and economic impacts. For this paper, many will be part of your case file in the near future. With the emergence of new tech, a licensed attorney needs to maintain the requisite knowledge and skill to comply with all continuing legal education requirements to which the lawyer is subject. As criminal defense attorneys we need to “…keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology….” 5

5 https://www.americanbar.org/groups/law_practice/resources/tech-report/2024/2024-technology-and-

training/

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Emerging technologies in the news include advanced Artificial Intelligence (AI), including generative AI and agentic AI, the continued development of autonomous vehicles and drone technology, innovations in biotech and sustainable energy like green steel and alternative livestock feeds, and the expansion of quantum computing for applications in finance and materials

science. Other topics in the tech news cycle involve the push for AI in scientific discovery, the militarization of Silicon Valley, the challenges of cybersecurity and data privacy, and how technologies like extended reality (XR) are shaping computing and the metaverse.

Per an American Bar Association (ABA) survey on lawyers and technology, “…Ninety-six percent of firms of 100 or more attorneys and 56% from firms of 10-49 attorneys report that they turn first to internal technical support staff. Conversely, only 4% of solo attorneys and 25% from firms of 2-9 attorneys responded this way.

Solo attorney respondents were most likely (28%) to report first turning to Google or some other non-legal specific online resource when there is a problem with their firm’s technology.

As expected, respondents from firms of 100 or more attorneys were least likely (4%) to report doing so.

The prevalence of technology-related issues points to the importance of technology training for lawyers….” 6 (emphasis added).

Per the ABA survey, lawyers were less likely to seek training on emerging technologies. “…Emerging Technologies Seen as Less Important

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Respondents were asked how important it is to them to receive training/education on emerging technology (e.g., blockchain, AI). Overall, 28% of respondents indicate that receiving training/education on emerging technology is very important.

This represents a slight increase over the previous year, and continues the upward trend of recent years (25% in 2023, 19% in 2022, and 21% in 2021). Forty-five percent report training/education on emerging technology is somewhat 6 Id.

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important (compared with 43% in 2023, 47% in 2022, and 44% in 2021). Fifteen percent reported it was neither important nor unimportant; 8% reported that it is not very important, and 4% indicated it being not at all important…” (emphasis added).

To identify the latest emerging technologies, I implemented two AI’s, ChatGPT5 and Google Gemini. As discussed before, AI output is only as good as the input databases the AI relies on. If the database the AI relies upon is biased, the results will be biased. 7 Always question the sources of information that created the databases. a. AI in Law Enforcement

Use of artificial intelligence tools by police and prosecutors to assist investigations, facial recognition, predictive policing, and even evidence sorting. AI-generated evidence (e.g., facial recognition hits) may be flawed or biased. Defense lawyers must know how to challenge AI reliability and demand algorithmic transparency. b. Geofencing Warrant

Warrants that compel tech companies (like Google) to provide data on all devices within a geographic area during a specific time frame. Raises Fourth Amendment concerns about mass surveillance. Defense must know how to challenge overbroad or vague geofence warrants. c. Body-Worn Camera Analytics

d. Voice Recognition & Audio Forensics

Technology used to identify suspects or verify statements via voice patterns or ambient sound. Increasing use in undercover recordings or smart device 7 ChatGPT.com search query “emerging trends in technology that a criminal defense lawyer needs to know”.

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AI and video analysis tools applied to police bodycam footage to automate review or detect specific behaviors. Could miss context or nuance in key moments. Defense may need independent review and challenge automated interpretations.

evidence. Lawyers need to know how to verify authenticity and expose potential manipulation. e. Mobile App & Social Media Surveillance

Law enforcement monitoring of apps like Facebook, Instagram, TikTok, Snapchat, or even encrypted apps for communication patterns. Many cases now involve digital footprints from social media or messaging platforms. Lawyers must understand privacy policies, data retention, and metadata implications. f. Deepfakes & Synthetic Media

Digitally altered videos or audio recordings created using AI to fabricate events, often indistinguishable from real ones. Raises concerns about authenticity of video evidence. Defense may need to hire experts to verify or debunk manipulated media. g. Internet of Things (IoT) Evidence

Data from connected devices like smart thermostats, watches, doorbells (Ring), or fitness trackers used in criminal cases. Can reveal timelines, movement, or presence at a scene. Lawyers must understand how to subpoena or suppress such data.

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h. Neurotechnology & Cognitive Privacy

Emerging brain-monitoring tools and brain-computer interfaces that may be used for lie detection or cognitive testing. Raises novel legal and ethical questions. Defense must be ready to argue constitutional limits on "mental privacy." i. Real-Time Surveillance & Drones

Use of drones and live surveillance (including from private networks like Amazon’s Ring) in policing. Surveillance may be continuous and warrantless.

Understanding limits and admissibility is essential for motion practice. j. Algorithmic Sentencing & Risk Assessment Tools

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Software used to calculate recidivism risk or guide sentencing recommendations. These tools may perpetuate systemic bias. Defense must be prepared to challenge these assessments with expert witnesses or constitutional arguments. k. Data Aggregation & Digital Footprint Profiling

The collection of vast data from consumer behavior, browsing history, purchases, and location used to build behavioral profiles. Increasingly used in investigations or character assessments. Defense must understand what’s admissible, what’s hearsay, and how to suppress or rebut. l. Artificial Intelligence (AI) and Machine Learning

News features advancements in generative AI for tasks like content personalization, code generation, and creating specific AI chatbot experiences.

Businesses are investing heavily in AI for infrastructure, with companies like Microsoft and OpenAI developing advanced AI models for enterprise applications. The U.S. Department of Energy is funding initiatives to use AI for scientific discovery, energy research, and national security. m. Transportation & Robotics

Legal developments in China are enabling driverless cars, while the broader trend includes companies using drones for delivery and developing autonomous military systems. Companies like Joby Aviation are pursuing electric aviation, with one company planning to acquire a helicopter business for its passenger operations. n. Biotechnology & Sustainability

o. Quantum Computing & Hard Tech

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Focus is on alternative livestock feeds and other sustainable technologies to help manage carbon emissions. News covers advancements in stem cell therapies and other biotechnologies, with the goal of accelerating innovation in drug and material development.

Companies are developing quantum computing services, or "QaaS," and using quantum efforts to improve networking and materials science. The tech industry is shifting towards "hard tech," which involves AI-powered advancements in hardware and complex engineering, rather than solely consumer web services. p. Extended Reality (XR)

The development of XR technologies, such as smart glasses, continues, though companies are still working to deliver on their full potential for future computing experiences.

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V.

TECH ETHICS

At this point we are caught up on the tech terms, right? And we know to be fearful of emerging trends in tech, right? BUT…HOW DOES THIS IMPACT ME AS A LAWYER? We must be competent in the law, which means we must understand the law and how it is applied. Further, our ethical obligation requires that we be competent in technology and emerging tech. Please remember that your experience, use, and implementation of technologies will be treated as an extension of you as a lawyer. You have a duty to perform Technology & Criminal Defense (version 3)

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diligence, to verify the data you use, and a duty of candor to the court. Failure to perform your duty can present great harm to your client and your license to practice law. Remember, we as lawyers must ensure that what we submit is true and not misleading. Errors or omissions by counsel will not be a great defense in a grievance hearing. Remember, tools are aids, not replacements – always manually verify information obtained from automated sources.

Remember, misuse of automation (like fabricated or unverified information) can be treated as misconduct. a. SIX TIPS TO SURVIVE ETHICS COMPLAINTS ON TECH & AI 1. Always verify AI- Generated Content: Fact-check every cited case, quotation, and citation.

2. Use established providers for legal research (Westlaw, Lexis, FastCase, etc.) rather than general-purpose large language models (LLM’s) for legal research.

3. Implement Internal Policies: Create an oversight review process that provides an internal checklist for staff to verify information obtained from automated tools.

4. Stay Informed: Become competent on the Ethical Rules, standing orders, and local rules of your Court/Judge regarding the disclosure of automated tech.

5. Educate and Train: Train your team in the Ethical Rules, standing orders, and local Rules of your Court/Judge regarding automation.

6. Document. Document. Document. If challenged, these processes can mitigate responsibility if something goes wrong. b. SBOT Professional Ethics Committee & Texas Disciplinary Rules of Professional Conduct (TDRPC) i. April 2025, Ethics Opinion 7058

8 Tex. Comm. On Professional Ethics, Op. 705 (2025)

https://www.legalethicstexas.com/resources/opinions/opinion-705/

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It is very important for the practitioner in the technological age to understand the following SBOT and the ABA rules and ethics opinions.

Q: What ethical issues are raised under the TDRPC by a lawyer’s use of generative artificial intelligence in the practice of law?

Conclusion: While there may be many ways that generative AI can assist in the practice of law and benefit lawyers and clients alike, Texas lawyers must always be aware of the ethical issues that may arise in the use of generative AI. Among many other issues, lawyers should acquire basic technological competence before using any generative AI tool, should always ensure that the tool does not imperil confidential client information, should always verify the accuracy of any responses received from a generative AI tool, and should not charge clients for the time “saved” by using a generative AI program. ii.

January 2020, Ethics Opinion 686

Q: May a Texas lawyer practice law as an associate or other non-partner firm lawyer—and the only lawyer in the Texas office—of a law firm whose partners are only licensed to practice law outside of Texas?

Conclusion: A Texas lawyer may practice law as an associate or other nonpartner firm lawyer—and the only lawyer in the Texas office—of an out-ofstate law firm, provided that the associate or other non-partner lawyer: (1) qualifies as a “firm lawyer” reasonably considered to be “in” the law firm under the factors discussed in Opinion 577; and (2) is given the responsibility and authority to make decisions about the firm’s practice of law in Texas. The outof-state law firm should also have a resident agent for service of process in Texas.

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iii.

September 2018, Ethics Opinion 6809

Q: Under the TDRPC may a lawyer use cloud-based client data storage systems or use cloud-based software systems for the creation of client-specific documents where confidential client information is stored or submitted to a cloud-based system?

9 Tex. Comm. On Professional Ethics, Op. 680 (2018)

https://www.legalethicstexas.com/resources/opinions/opinion-680/

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Conclusion: Under the TDRPC, a lawyer may use a cloud-based electronic data storage system or cloud-based software document preparation system to store confidential information or prepare legal documents. However, lawyers must remain alert to the possibility of data breaches, unauthorized access, or disclosure of client confidential information and undertake reasonable precautions in using those cloud-based systems. iv.

April 2015, Ethics Opinion 64810

Q: Under the TDRPC, may a lawyer communicate confidential information by email?

Conclusion: Under the TDRPC, and considering the present state of technology and email usage, a lawyer may communicate confidential information by email.

Some circumstances, may, however, cause a lawyer to have a duty to advise a client regarding risks incident to the sending or receiving of emails arising from those circumstances and to consider whether it is prudent to use encrypted email or another form of communication. v.

TDRPC 1.0111

(a) A lawyer shall not accept or continue employment in a legal matter which the lawyer knows or should know is beyond the lawyer's competence, unless:

(1) another lawyer who is competent to handle the matter is, with the prior informed consent of the client, associated in the matter; or

(b) In representing a client, a lawyer shall not:

10 https://tcle-web.s3.amazonaws.com/public/documents/Opinion-648_kwgdhBb.pdf

11 Texas Disciplinary Rules of Professional Conduct. § 1.01, (Texas Center for Legal Ethics, 2025) from https://legalethicstexas.com/resources/rules/texas-disciplinary-rules-of-professional-conduct/competentand-diligent-representation/ (last visited Oct 07, 2025) https://www.legalethicstexas.com/resources/rules/texas-disciplinary-rules-of-professionalconduct/competent-and-diligent-representation/

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(2) the advice or assistance of the lawyer is reasonably required in an emergency, and the lawyer limits the advice and assistance to that which is reasonably necessary in the circumstances.

(1) neglect a legal matter entrusted to the lawyer; or

(2) frequently fails to carry out completely the obligations that the lawyer owes to a client or clients.

(c) As used in this Rule neglect signifies inattentiveness involving a conscious disregard for the responsibilities owed to a client or clients.

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Accepting Employment Comment 1. A lawyer generally should not accept or continue employment in any area of the law in which the lawyer is not and will not be prepared to render competent legal services. Competence is defined in Terminology as possession of the legal knowledge, skill, and training reasonably necessary for the representation. Competent representation contemplates appropriate application by the lawyer of that legal knowledge, skill and training, reasonable thoroughness in the study and analysis of the law and facts, and reasonable attentiveness to the responsibilities owed to the client.

Comment 2. In determining whether a matter is beyond a lawyer's competence, relevant factors include the relative complexity and specialized nature of the matter, the lawyer's general experience in the field in question, the preparation and study the lawyer will be able to give the matter, and whether it is feasible either to refer the matter to or associate a lawyer of established competence in the field in question. The required attention and preparation are determined in part by what is at stake; major litigation and complex transactions ordinarily require more elaborate treatment than matters of lesser consequences.

Comment 3. A lawyer may not need to have special training or prior experience to accept employment to handle legal problems of the type with which the lawyer is unfamiliar. Although expertise in a particular field of law may be useful in some circumstances, the appropriate proficiency in many instances is that of a general practitioner. A newly admitted lawyer can be as competent in some matters as a practitioner with long experience. Some important legal skills, such as the analysis of precedent, the evaluation of Technology & Criminal Defense (version 3)

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evidence and legal drafting, are required in all legal problems. Perhaps the most fundamental legal skill consists of determining what kind of legal problems a situation may involve, a skill that necessarily transcends any particular specialized knowledge.

Comment 4. A lawyer possessing the normal skill and training reasonably necessary for the representation of a client in an area of law is not subject to discipline for accepting employment in a matter in which, in order to represent the client properly, the lawyer must become more competent in regard to relevant legal knowledge by additional study and investigation.

If the additional study and preparation will result in unusual delay or expense to the client, the lawyer should not accept employment except with the informed consent of the client.

Comment 5. A lawyer offered employment or employed in a matter beyond the lawyer's competence generally must decline or withdraw from the employment or, with the prior informed consent of the client, associate a lawyer who is competent in the matter. Paragraph (a)(2) permits a lawyer, however, to give advice or assistance in an emergency in a matter even though the lawyer does not have the skill ordinarily required if referral to or consultation with another lawyer would be impractical and if the assistance is limited to that which is reasonably necessary in the circumstances.

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Comment: Competent and Diligent Representation Comment 6. Having accepted employment, a lawyer should act with competence, commitment, and dedication to the interest of the client and with zeal in advocacy upon the client's behalf. A lawyer should feel a moral or professional obligation to pursue a matter on behalf of a client with reasonable diligence and promptness despite opposition, obstruction, or personal inconvenience to the lawyer. A lawyer's workload should be controlled so that each matter can be handled with diligence and competence. As provided in paragraph (a), an incompetent lawyer is subject to discipline.

Comment: Maintaining Competence 8. Because of the vital role of lawyers in the legal process, each lawyer should strive to become and remain proficient and competent in the practice of law, including the benefits and risks associated with relevant technology.

To maintain the requisite knowledge and skill of a competent practitioner, a lawyer should engage in continuing study and education. If a system of peer review has been established, the lawyer should consider making use of it in appropriate circumstances. Isolated instances of faulty conduct or decision should be identified for purposes of additional study or instruction.

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vi. See also TDRPC 1.03 - Communication vii. See also TDRPC 1.05 – Confidentiality of Information viii. See also TDRPC 1.14 – Safekeeping Property ix. See also TDRPC 5.05 – Unauthorized Practice of Law c. American Bar Association Ethics Opinions & Model Rules i. July 2024, ABA Formal Opinion 51212:

Conclusion: Lawyers using Generative Artificial Intelligence Tools have a duty of competence, including maintaining relevant technological competence, which requires an understanding of the evolving nature of GAI. In using GAI tools, lawyers also have other relevant ethical duties, such as those relating to confidentiality, communication with a client, meritorious claims and contentions, candor toward the tribunal, supervisory responsibilities regarding others in the law office using the technology and those outside the law office providing GAI services, and charging reasonable fees. With the ever-evolving use of technology by lawyers and courts, lawyers must be vigilant in complying with the Rules of Professional Conduct to ensure that lawyers are adhering to their ethical responsibilities and that clients are protected.

Comments: To ensure clients are protected, lawyers using generative artificial intelligence tools must fully consider their applicable ethical obligations, including their duties to provide competent legal representation, to protect client information, to communicate with clients, to supervise their employees 12 https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-

opinions/aba-formal-opinion-512.pdf

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and agents, to advance only meritorious claims and contentions, to ensure candor toward the tribunal, and to charge reasonable fees. ii.

November 2022, ABA Formal Opinion 503 13

Conclusion: Absent special circumstances, lawyers who copy their clients on emails or other forms of electronic communication to counsel representing another person in the matter impliedly consent to a “reply all” response from the receiving counsel. Accordingly, the reply all communication would not violate Model Rule 4.2. Lawyers who would like to avoid consenting to such communication should forward the email or text to the client separately or inform the receiving counsel in advance that including the client on the electronic communication does not constitute consent to a reply all communication.

Comments: In the absence of special circumstances, lawyers who copy their clients on an electronic communication sent to counsel representing another person in the matter impliedly consent to receiving counsel’s “reply all” to the communication. Thus, unless that result is intended, lawyers should not copy their clients on electronic communications to such counsel; instead, lawyers should separately forward these communications to their clients. Alternatively, lawyers may communicate in advance to receiving counsel that they do not consent to receiving counsel replying all, which would override the presumption of implied consent. iii.

March 2021, ABA Formal Opinion 49814:

13 https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-

opinions/aba-formal-opinion-503.pdf

14 https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-

opinions/aba-formal-opinion-498.pdf

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Conclusion: The ABA Model Rules of Professional Conduct permit lawyers to conduct practice virtually, but those doing so must fully consider and comply with their applicable ethical responsibilities, including technological competence, diligence, communication, confidentiality, and supervision.

iv.

September 2018, ABA Formal Opinion 482 15

Conclusion: Lawyers must be prepared to deal with disasters. Foremost among a lawyer’s ethical obligations are those to existing clients, particularly in maintaining communication. Lawyers must also protect documents, funds, and other property the lawyer is holding for clients or third parties.

Comments: The Rules of Professional Conduct apply to lawyers affected by disasters. Model Rule 1.4 (communication) requires lawyers to take reasonable steps to communicate with clients after a disaster. Model Rule 1.1 (competence) requires lawyers to develop sufficient competence in technology to meet their obligations under the Rules after a disaster. Model Rule 1.15 (safekeeping property) requires lawyers to protect trust accounts, documents, and property the lawyer is holding for clients or third parties. Model Rule 5.5 (multijurisdictional practice) limits practice by lawyers displaced by a disaster.

Model Rules 7.1 through 7.3 limit lawyers’ advertising directed to and solicitation of disaster victims. By proper advance preparation and planning and taking advantage of available technology during recovery efforts, lawyers can reduce their risk of violating the Rules of Professional Conduct after a disaster.

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v.

May 2017, ABA Formal Opinion 477R 16

Conclusion: Rule 1.1 requires a lawyer to provide competent representation to a client. Comment [8] to Rule 1.1 advises lawyers that to maintain the requisite knowledge and skill for competent representation, a lawyer should keep abreast of the benefits and risks associated with relevant technology. Rule 1.6(c) requires a lawyer to make “reasonable efforts” to prevent the inadvertent or unauthorized disclosure of or access to information relating to the representation.

Comments: A lawyer generally may transmit information relating to the representation of a client over the internet without violating the Model Rules

15 https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-

opinions/aba-formal-opinion-482.pdf

16 https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-

opinions/aba-formal-opinion-477.pdf

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of Professional Conduct where the lawyer has undertaken reasonable efforts to prevent inadvertent or unauthorized access. However, a lawyer may be required to take special security precautions to protect against the inadvertent or unauthorized disclosure of client information when required by an agreement with the client or by law, or when the nature of the information requires a higher degree of security. vi.

ABA Model Rule 1.1 Comment 8

Client-Lawyer Relationship: A lawyer shall provide competent representation to a client. Competent representation requires the legal knowledge, skill, thoroughness, and preparation reasonably necessary for the representation.

Comment 8: To maintain the requisite knowledge and skill, a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology, engage in continuing study and education and comply with all continuing legal education requirements to which the lawyer is subject. vii.

ABA Model Rule 1.3 – Diligence

A lawyer shall act with reasonable diligence and promptness in representing a client. viii. See also ABA Model Rule 1.4 – Communications ix. See also ABA Model Rule 1.6 – Client-Lawyer Relationship d. Malpractice Hard Knocks and Lessons Learned

The issue has become so prevalent that courts are reminding parties and Attorneys of the potential investigation into violations of State Bar Rules due to fake, altered, or hallucinated arguments and cases.

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How to tell if its Fake

Failure to remain competent on technologies and emerging technologies will result in an ethical failure. The judicial system is seeing a rise in fake facts and fake law. And many of these cases involve licensed attorneys who relied upon technology.

Federal Judge Brantley Starr of the Northern District of Texas now requires the following certification on pleadings: CERTIFICATE REGARDING JUDGE-SPECIFIC REQUIREMENTS

I, the undersigned attorney, hereby certify that I have read and will comply with all judge-specific requirements for Judge Brantley Starr, United States District Judge for the Northern District of Texas.

I further certify that no portion of any filing in this case will be drafted by generative artificial intelligence or that any language drafted by generative artificial intelligence— including quotations, citations, paraphrased assertions, and legal analysis—will be checked for accuracy, using print reporters or traditional legal databases, by a human being before it is submitted to the Court. I understand that any attorney who signs any filing in this case will be held responsible for the contents thereof according to applicable rules of attorney discipline, regardless of whether generative artificial intelligence drafted any portion of that filing.

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[ATTORNEY NAME(S)] Unfortunately, there are many, MANY examples of sanctionable conduct by pro se, solos and large law firms. Dozens of cases where filings include false or made-up case citations, or misattributed quotes, because lawyers trusted AI or insufficiently vetted sources. Judges are imposing sanctions, fines, reprimands; some court systems are considering or implementing disclosure requirements if AI is used. All users of AI must understand risk of hallucination; vet all information; check citations; use reliable legal databases; consider policy or ethical guidance in jurisdiction. Even where oversight is expected, AI misuse can slip through. Even reputable, large firms can make serious mistakes. Always verify any authority cited. Internal controls are essential.

Here are some recent examples of sanctionable conduct:

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• Ferris III. v. Amazon.com, 778 F.Supp.3d 879, 880, 882 (Dist. Court, ND Mississippi, 2025) Case involved the citation of false authority and the use of generative artificial intelligence ("AI"). Plaintiff sued his employer Amazon.com Services, LLC, in state court alleging misappropriation of intellectual property, retaliation, defamation, and hostile work environment. Plaintiff cited a case that did not exist. After removal to Federal Court by Amazon, a motion to dismiss stated that Plaintiff relied upon non-existent case law for his claim. The Plaintiff then responded to the motion by hallucinating six fake cases. The Court found that Plaintiff violated Rule 11 of the Federal Rules of Civil Procedure and ordered him to show cause as to why his case should not be dismissed. That same day, three filings by Plaintiff arrived by mail containing false or misleading citations.

A show-cause hearing was held during which Plaintiff informed the Court that he had drafted the problematic filings using AI. The statement Plaintiff then argued with the Court was also drafted by AI. Holding: “…Plaintiff is hereby ordered to pay costs incurred by Defendant Amazon.com Services, LLC. Defendant shall submit a detailed bill of costs, with accompanying affidavit, showing what costs and expenses were incurred as a reasonable result of Plaintiff's false citations. Once the bill of costs and affidavit have been submitted, this Court will determine the exact amount Plaintiff owes in a subsequent order.”

• Ex Parte Allen Michael Lee, 673 S.W.3d 755 (Tex. Ct. App. – Waco, 2023)

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Appellant cited three published cases in support of his argument and represented to the Court that the published cases stood for certain propositions. However, none of the cases existed. Holding: “The failure to adequately brief an issue presents nothing for us to review, and we are not required to make an appellant's arguments for him. See TEX. R. APP. P. 38.1(i); Lucio v. State, 351 S.W.3d 878, 896 (Tex. Crim. App. 2011) (citing Busby v. State, 253 S.W.3d 661, 673 (Tex. Crim. App. 2008)); see also Neville v. State, 622 S.W.3d 99, 104 (Tex. App.-Waco 2020, no pet.).

Accordingly, because Lee inadequately briefs his sole issue, it presents nothing for our review and is overruled.”

• Mata v. Avianca, 678 F.Supp.3d 443 (U.S. District Court S.D. New York, 2023)

Respondents submitted fake case citations and arguments. Respondents were given an opportunity to “come clean about their actions” after Defendants called out the improper citations. Instead the Respondent’s “doubled down” on their assertions. The Court issued an Order to Show Cause why one of the individual Respondents ought not be sanctioned. The Court found bad faith on the part of the individual Respondents based upon acts of conscious avoidance and false and misleading statements to the Court. The Court then imposed sanctions on the individual Respondents and imposed sanctions jointly on the Respondent’s Firm. The sanctions were "limited to what suffices to deter repetition of the conduct or comparable conduct by others similarly situated."

Rule 11(c)(4). The sanctions included

How to tell if its Fake

“…a. Within 14 days of this Order, Respondents shall send via first-class mail a letter individually addressed to plaintiff that identifies and attaches this Opinion and Order, a transcript of the hearing of June 8, 2023, and a copy of the April 25 Affirmation, including its exhibits.

b. Within 14 days of this Order, Respondents shall send via firstclass mail a letter individually addressed to each judge falsely identified as the author of the fake "Varghese", "Shaboon", "Petersen", "Martinez", "Durden" and "Miller" opinions. The letter shall identify and attach this Opinion and Order, a transcript of the hearing of June 8, 2023, and a copy of the April 25 Affirmation, including the fake "opinion" attributed to the recipient judge. c. Within 14 days of this Opinion and Order, respondents shall file with this Court copies of the letters sent in compliance with (a) and (b).

d. A penalty of $5,000 is jointly and severally imposed on Respondents and shall be paid into the Registry of this Court within 14 days of this Opinion and Order…”

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• In re Martin, 670 BR 636 (Bankr. Court, ND Illinois 2025) Federal Judge issued an order directing two attorneys and their firms to show cause why they should not be sanctioned for filing a brief containing fake quotations and nonexistent authority manufactured by artificial intelligence and why their compensation does not exceed the reasonable value of their services pursuant to 11 U.S.C. § 329. The Attorneys withdrew the offending brief and separately responded to the Federal Judge’s show cause order. An offending Attorney also withdrew an application for compensation in this case.

The United States Trustee and Chapter 13 Trustee asked the Judge to sanction the offending Attorneys. The Court appreciated the remorse and candor of the offending parties and found each violated Federal Rule of Bankruptcy Procedure 9011. The Court ordered joint-and-several sanction of $5,500, along with a requirement that the offending parties attend an upcoming course on the dangers of AI scheduled for the National Conference of Bankruptcy Judges (NCBJ) annual meeting in September, as the least harsh sanction that will appropriately address counsel's conduct and deter future, similar misconduct from them and others.

17 Texas lawyer fined for AI use in latest sanction over fake citations | Reuters 18 gov.uscourts.insd.215482.99.0.pdf

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How to tell if its Fake

• Other notable examples: • In November 2024, in Beaumont, Texas, an attorney was fined $2,000 and ordered to attend a course on generative AI after submitting a federal court filing that included citations to non-existent cases. Judge noted that the Attorney failed to verify the existence of these cases before filing. The Attorney attributed the error to using an AI-based legal research tool and has since implemented internal policies to prevent future occurrences. 17 • In February 202518, a Federal Magistrate Judge recommended sanctions for a Texas Attorney after he acknowledged using generative AI to draft briefs without verifying the cited cases. The Federal Judge emphasized that, given the availability of tools like LexisNexis and Westlaw, there was no excuse for failing to verify citations. Sanctions were issued in the amount of $15,000.00 pursuant to Federal Rule of Civil Procedure 11 and was referred to the Chief

•

•

•

•

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•

Judge for disciplinary enforcement. The Attorney was ordered to present to his client the Court’s order.

A large firm filed a lawsuit that included several legal cases that don’t exist — the result of using generative AI (ChatGPT) to find precedent. The firm issued internal warnings. The lead attorney admitted using AI and apologized. Risk of sanctions. The firm now has requirements / controls (e.g. verifying AI output) in place.

Attorneys submitted a brief with fictitious legal research — several cases cited didn’t exist; they relied on ChatGPT and didn’t properly check. Judge fined them $ 5,000. The court emphasized attorneys’ “gatekeeping role” under ethical / procedural rules.

An attorney from a firm used ChatGPT to generate citations in a lawsuit over prison conditions. The citations turned out to be fabricated.

The case is under scrutiny. The judge is considering sanctions. The incident is getting publicity and raising alarms about AI misuse in high-stakes civil rights litigation.

A plaintiff’s brief included AI-fabricated cases. A judge admitted he almost included one of those fake citations in a ruling before verifying.

The court sanctioned the plaintiff’s law firm (monetary penalties). The judge issued orders emphasizing verifying legal authority. Even judges can be misled; highlights that tools can mislead unintentionally; underscores the importance of fact-checking, not just trusting AI or slick briefs.

Lawyers in the UK cited nonexistent cases or invalid quotes, apparently relying on AI or AI-adjacent tools. The High Court issued a regulatory warning / guidance. UK courts are emphasizing disciplinary risk, wasted costs, possibility of contempt. Lawyers / firms being notified of duties. This is not just a U.S. problem; global courts are reacting. Ethical / professional obligations cross borders; one cannot assume AI-driven citations are safe because they look plausible.

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VI.

HARDWARE & SOFTWARE FOR THE TRIAL LAWYER

So, what do you need to survive this tech age? How do you stay competent?

How do you train your staff? Where will you be using technology? How do you secure your client’s data? How do you maintain the attorney-client privilege? a. Tech Considerations

Before implementing any tech, go through this list to brainstorm what you actually need:

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metadata preservation. Court may scrutinize forensic methods heavily (especially with mobile/device data). o Admissibility / certified transcript options: Whether the transcript/video sync / proof is acceptable. Sometimes you need human transcription or certification.

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o Accuracy with “noisy” audio/video: Many legal recordings are imperfect (background noise, overlapping speakers, low volume). See sample clips or trials. o Admissibility & Chain of Custody: If you’re imaging drives, ensure device/software produces cryptographic hash, write-blocker use; Proof of

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o Annotations & Callouts: Being able to highlight, zoom, spotlight parts of documents.

Easily switch between documents / exhibits. o Citation / Case Treatment / Shephardizing / KeyCite: Being able to check negative treatment (reversed, overruled, etc.) is critical—how good is that module? Check your content needs vs the plan. For criminal defense in Texas, you’ll need good coverage of Texas appellate / criminal decisions, but also relevant federal and adjacent state decisions, plus strong citator and negative treatment tools. o Collaboration (Internal + with Clients / Co-Counsel / Experts): Client portals; Shared document editing / review. Task assignment, dashboards. o Cost & Scalability: Upfront + ongoing fees (per user, seats, storage). Add-on costs. Support & maintenance. o Cost / Licensing: Upfront license cost + annual fees; Number of seats/users. Cost per hour / minute — and whether there are subscription or usage fees, extra for sync/video, etc.

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o Cost vs Use: For most, digital forensics is done via hiring experts. But for day-to-day case review, having good software for organizing, review, and presentation can drastically reduce expert fees / prep time. o Data Storage: Handling large volumes of discovery: video, audio, documents (PDF, emails, transcripts), mobile device data, metadata. Secure storage; chain of custody; versioning; audit logs. o Data Volume & Storage Costs: Video and audio evidence are large; storage (cloud or on-premise), backups, encryption, retention policies matter (especially with federal discovery rules). o Depth of Secondary Material: Things like treatises, practice guides, form books, annotations, jury pattern charges, local case annotations are often what separates cheaper/free tools from premium. o Device extraction / mobile forensics when needed. o Document Automation & Version Control: Ability to build templates (motions, discovery requests, grants, etc.), auto-populate client/case info.

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/ Lexis if your lower-cost tool misses something. o Licensing / Seats / User Management: Whether one attorney seat gives others some access; how many users fit under one subscription; “out-of-plan” content fees; mobile vs desktop vs offline access; contract length.

Negotiate licensing smartly.

Premium vendors often charge per headcount or seat; see whether you can limit full seats to heavy users and provide restricted or read-only access to others. Watch out for hidden fees / overage charges: Sometimes “out-of-plan” content (e.g. specialized treatises, historical content) triggers extra fees.

Always read the fine print. o Multimedia support: Video depositions, audio, images, PDFs; Ability to clip video/audio, sync with transcripts. o Organizational: Organizing and evidence, building tagging timelines, linking facts, persons, documents. o Portability & Collaboration: Access on multiple devices; Share with co-counsel/expert; Cloud sync or file sync (Dropbox, etc.).

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Track versions so you know who changed what & when. o Exhibit / Document Organization: Exhibit numbering/stickers; Filtering by admitted / not admitted; tagging. o Export & clip functionality: Can you make video or audio clips tied to transcript segments for exhibits, hearings, impeachment, etc. o Exportability / Presentation Format: Can you export exhibits in formats usable in court (video clips, stills, transcripts, redacted versions)? o Hardware tools: write blockers, forensic imagers, secure drives. o Investigation / Evidence Management: Tools to store, tag, review evidence / discovery materials. Ability to redact or share securely. Audit trails. o Jurisdiction Coverage: Make sure it covers Texas statutes, appellate decisions, venue court opinions (Criminal, Civil, etc.), and Texas-specific secondary sources and practice guides.

Layer in a premium tool only as needed.

For many trials, important precedents, or motions, you may want Westlaw

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o Presentation Mode: Tablet / iPad or touchscreen support; Supports courtroom AV (HDMI, projectors); Reliable offline mode. o Reliability & Speed: Quick startup, fast navigation among exhibits; Good support if something fails mid-trial. o Searchability / AI / Indexing: Tools that index transcripts, allow keyword search, timeline generation, link persons to statements, etc., save a lot of review time. o Security & Compliance: Encryption at rest/in transit.

Access controls. Audit logs. Data residency (some cloud services may store data in places subject to different laws). o Security / privacy: encrypted storage, chain of custody, who has access, audit logs. o Security, Confidentiality, and Compliance: Encryption (in transit and at rest). Secure client communication. Permissions / access controls. Audit logs. o Speed & User Interface: Efficiency in search & filtering, ability to upload your documents or use AI tools can save huge time.

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o State and Local Support: Texas Penal Code, Code of Criminal Procedure, local court rules/forms.

Local court deadlines / scheduling, bail & time served tracking, priors / sentencing enhancements, etc.

Start with what you already qualify for (e.g. if your State Bar membership gives you free Fastcase / Casemaker). o Support & Training: Determine responsiveness of each vendor, whether they provide training in how to use the tool well, and support for upgrades/updates. o Time / Billing Features: Accurately capture billable / non-billable time; Flexible billing rates (e.g. for different attorneys, special arrangements); Integration with trust accounting, payment processing. o Total Cost of Ownership: Besides subscription, consider hardware, Internet, backup/archiving, security compliance, etc. o Training / Support: Tutorials, trial runs; Good vendor support. o Transcription / audio-to-text: redaction; preparing exhibits. o Transcription, Redaction & Privacy: Sensitive audio (e.g.

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witness statements) might require redaction or protection; ensure tools support that; Protected communications; know state/federal laws about privacy and recording. o Turnaround time: how fast do you get a usable transcript? For rough AI vs final human-proofed.

o Usability / Adoption: Ease of onboarding, staff training.

Customization vs too much complexity. Mobile access. o User interface / ease of use: being able to easily search, highlight, share, tag; collaboration features with co-counse

b. Hardware Checklist Following are essential devices for every lawyer’s trial bag.

o Microphone, Mic Lapel and Stand o Monitors o Mouse & Keyboard (Bluetooth) o Plaud Note AI Voice Recorder o Printers and Label Makers o Projectors & Screen o OCR Scanner o Speaker o SSD / HD / USB Storage o Tablet (iPad Pro, Surface, etc.) o Voice Recordings o Webcam o Wireless presentation tools

c. Software Checklist Following are essential software, free and paid, that can make your legal practice better now.

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How to tell if its Fake

o AI Camera o AV Cart o AirPods with AI o Cell Phone o Green/Red Laser Clicker o Computers o Digital Camera o Document Camera / Visualizer o Dongles o Drones o Green Screen o Headphones o Laptop / Desktop (Windows or Mac) o Meta AI Glasses & Sunglasses

i. Top Free Software o “Texas All Statutes” Apps o CaseFox o Casemaker (via TexasBarCLE / State Bar access) o Fastcase (through State Bar of Texas membership) o FindLaw — Texas Statutes & Constitution o Free Resources (Google Scholar, State Bar Libraries, Law Libraries)

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ii.

Top Paid Software o ACS4Legal (Courtroom Objections App) o Adobe Express o Adobe Firefly o Adobe Premier Elements o Canva o Case Crafter o Casefleet o CaseSync o Casetext (CoCounsel Core, etc.) o ChatGPT5 – Deep Research with Dropbox Integration o ChatGPT5 Picture Generator for Voir Dire and Speech Power Points o Clio (mobile app + cloud platform) o Clio Drive o Cloud Storage Security o CROSStrax o Document Preparation Software like Microsoft Word: Technology & Criminal Defense (version 3)

o Law Library Services, County & Public Law Libraries o LawStack o State Law Library of Texas (“Texas State Law Library”) o TexasLawHelp / FindLaw / Legal Information Institute (LII) o TxLaws2Go o Wave Accounting

o Dropbox for Business / Enterprise o EaseUS o EDiscovery o EFax o ExhibitView Trial Presenter o Fast Case AI o Fastcase / Casemaker o Filevine o Firm Central (Thomson Reuters) o Icecream Screen Recorder o iLitigate o I-Pro Cloude Evidence Software o JusticeText o Kodak Capture Pro Scanning Software License o Lexis Protégé AI Software o Lexis Advance, LexisNexis o Loom o Matey AI CrimD Page 45 of 63

o REV for Transcripts of Videos o Microsoft 365 o RingCentral, EVoice, Google o Microsoft 365 CoPilot Voice, etc. o Microsoft PowerPoint o Sanction (by LexisNexis) o Movavi o Skribe o MyCase o Smokeball o Of Counsel I.T. o Sonix Legal o Other Project / Task / o Texas Bar Legal LawBox Workflow tools (Wrike, Asana, o TrialDirector Trello etc.) o TrialPad: o OtterAI o TurboScribe o PatrolEyes Basic DEMS License o VEED o PDF suite o Video/Audio o Pinnacle Studios o VLC o PracticePanther o VOIP/Remote Calls o ProPresenter 7 o Wavel.ai o PS Express o Westlaw AI / Edge / Precision o PUSH: legal o Zoom for Business o RecCloud VII. BUSINESS MANAGEMENT TECH

a. MANAGEMENT

Practice management suites (case tracking, timekeeping, secure client portals) now include AI features for drafting and triage. These reduce overhead and speed client response times. But integrating AI into client communications raises duty-of-care, confidentiality, and supervision questions. The purpose of Technology & Criminal Defense (version 3)

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A solo attorney is only as good as the stability of his or her office. I have seen plenty of excellent trial lawyers burn out and quit because the management of a business became too overwhelming. Technology is not just a courtroom or investigation tool – it will change how efficiently you manage and grow your practice. Ensure when implementing any technology, that you adopt a vendor due diligence checklist (security, data residency, retention), build written policies for internal AI use, and train staff on document classification and privilege.

a management system is to centralize case management, calendaring, time tracking, billing and client communication. A system will also enhance collaboration with internal teams, investigators, experts and co-counsel. These systems include case management systems like Clio, PracticePanther and MyCase.

Document Management Systems (DMS) provide a secure storage site to organize, review, catalog and retrieve legal documents. DMS enables fast document search, uniform version updates and controls, compliance and data security, and typically integrate with practice management tools. These systems include NetDocuments, iManage, and Worldox.

E-Billing & Time Tracking Software Automates invoice generation, tracks billable hours, and simplifies client billing. Proper commitment to inputting time and billing will reduce billing errors, increase cashflow, enable clients to pay in a variety of ways electronically, and protect your law license by having a receipt for services rendered. These systems include TimeSolv, Bill4Time and Zola Suite

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Client Relationship Management (CRM) software manages client communication, lead generation, and intake processes. These systems help convert leads into clients with streamlined intake, tracking tools for interactions and marketing efforts and assist with personalizing client experiences.

These systems include Lawmatics, HubSpot (legalcustomized), and Clio Grow.

Legal AI & Automation Tools automate document review, contract generation, and legal research. These systems provide cost savings in time on routine tasks, increase accuracy and compliance, and aid in boosting productivity by attorneys and staff. These systems include Luminance, Casetext CoCounsel and Lawyaw.

Secure Communication & Collaboration Tools provide secure methods of virtually communicating with team members and clients. These tools include Slack (legal integration), Zoom (with meeting transcription) and Microsoft Teams.

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b. MARKETING Marketing and client intake benefit from CRM tools, chatbots, and analytics; those same tools create ethical issues around solicitation rules, advertising accuracy, and preserving privileged intake communications. Client relationship management (CRM) with intake forms that preserve privilege and consent.

Ethical use of analytics; avoid dark patterns and misleading claims; archive ads and site changes. Monitor reviews and social media; establish takedown/response protocols.

AI-Powered CRM & Marketing Automation provides law firms track leads, automate follow-ups, send targeted emails, and manage client intake.

Streamlines client acquisition and engagement while saving time. Law-focused CRMs like Clio Grow or Lawmatics are built with compliance and client confidentiality in mind. These systems include HubSpot, Clio Grow, Lawmatics, and Salesforce.

Search Engine Optimization (SEO) Tools improves your firm's visibility in Google searches for legal services. Most people begin looking for legal help via search engines. Can help you target relevant keywords like “divorce lawyer near me” or “personal injury attorney in [city].”

AI Chatbots & Live Chat engage potential clients on your website 24/7 by answering questions or collecting contact info. Immediate response increases conversion rates. Tools like Smith.ai are built specifically for law firms and can pre-screen leads.

Online Review & Reputation Management systems gather and showcase client reviews, and manage your online reputation. Reviews heavily influence client trust and decision-making. These systems help build credibility on Technology & Criminal Defense (version 3)

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Social Media Management & Content Marketing Platforms help create, schedule, and analyze social media posts and content (blogs, infographics).

These systems can help build trust with future clients, establish yourself as a leader in your trade and support brand visibility. Sharing digestible legal tips or case results (within ethical bounds) can attract and retain clients. Some systems include Hootsuite, Buffer, Jasper, and Canva.

platforms like Google and Avvo. Some systems include Birdeye, Podium, ReviewTrackers, Google Business Profile. c. ACCOUNTING

Integrated accounting with reconciliations, trust safeguards, and role separation. Automated payment links with surcharge/fee disclosures; ACH as default to reduce costs. Accounting and trust accounting are increasingly bundled into cloud solutions that reduce errors and automate compliance reporting — but firms must vet vendors’ security and backup protocols.

Clio Manage (with Clio Payments + Clio Grow) provides legal practice management software with built-in accounting and billing. These services allow for accurate time tracking, billing and invoicing, IOLTA Trust Compliance and full integration with Quickbooks Online and Xero. One of the nice features of this service is the robust financial reporting tool. Clio is based on customizable workflows special to each firm, but does require some user interfacing to create the customizations.

How to tell if its Fake

QuickBooks Online (with LeanLaw or Clio Integration) allows for Cloudbased accounting software widely used by small to mid-sized firms. An extension of the Intuit brands, Quickbooks is widely used by Accountants, CPAs and Bookkeepers. The best use of this software is to create routines to maintain general ledger, expenses, bank reconciliations and charts of accounts. The system provides for a comprehensive payroll processing and tax reporting service for federal and State filings. Also provides mechanisms for tracking payments to contractors and other vendors.

LeanLaw is a legal billing and trust accounting software that integrates with QuickBooks Online and provides time tracking, invoicing, LEDES billing, realtime syncing with QuickBooks and trust accounting with 3-way reconciliation.

Xero (with integrations like Actionstep or Smokeball) is Cloud-based accounting with a strong API for customization and provides services for bank feeds, invoicing, reconciliation and integration with practice management tools.

This is a cost-effective service for firms on budgets and has a very clean user interface.

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CosmoLex is an all-in-one legal practice management + accounting system.

The system provides built-in general ledger (no need for QuickBooks), trust accounting with compliance tools, billing, calendaring, and document management. Developed for law firms and simplifies case management by avoiding tech stack with third parties. d. FINANCE

Legal Accounting Software manages law firm-specific accounting, including trust accounting, IOLTA compliance, billing, and expense tracking. Ensures accurate financial records, compliance with bar rules, and better cash flow management. Tools like CosmoLex and LeanLaw are built specifically for law firms, ensuring legal billing codes and trust accounting are properly handled.

Time Tracking & Billing Software tracks billable hours, generates invoices, handles client payments, and integrates with accounting platforms. Capturing all billable time is essential to maximizing revenue and minimizing revenue leakage. Enables ABA task codes, LEDES billing formats, and custom rate management per client/matter. Some types of systems include TimeSolv, Bill4Time, Clio Manage, and Rocket Matter.

Expense Management & Reimbursement Tools tracks and categorizes firm expenses, simplifies reimbursements, and integrates with accounting platforms. Helps control overhead, improves tax documentation, and prevents missed deductions. Streamlines tracking of case-specific expenses for client billing and reimbursements. Some systems include Expensify, Rydoo, and Zoho Expense.

Online Payment & Trust Fund Management Tools enables secure client payments via credit card or ACH, including proper handling of trust deposits.

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How to tell if its Fake

Financial Reporting & KPI Dashboards provides insight into firm finances via dashboards for revenue, realization rates, collection rates, profit per partner, etc. Data-driven decisions improve firm profitability, efficiency, and strategic planning. Tailors KPIs to legal operations, e.g., profit per case type or aging A/R by client. These systems include Firm Dashboard (Clio), LawKPIs, PracticePanther, and Helm360.

Increases client convenience and speeds up cash flow while ensuring ethical compliance. Designed to meet ABA and state bar rules for trust fund (IOLTA) handling. Some systems include LawPay, Gravity Legal and Headnote. e. Human Resources

Human resources platforms make recruiting and performance management more efficient but also collect sensitive employee data that must be protected.

HR systems with least privilege access; separate personnel files; encrypt PII and health data. Security awareness training; phishing simulations; onboarding/offboarding checklists.

HR Management Systems (HRMS) / Human Capital Management (HCM) Platforms centralize employee records, provide onboarding and offboarding automation, time-off tracking and benefits management, and compliance reporting (important for legal environments). Some examples include BambooHR, Workday, UKG (Ultimate Kronos Group), and ADP Workforce Now.

Applicant Tracking Systems (ATS) streamlines recruitment and candidate screening, helps manage roles requiring legal qualifications and bar admissions, ensures structured interview workflows and EEO compliance.

Some examples include Greenhouse, iCIMS, Lever, JazzHR.

How to tell if its Fake

Performance Management Software facilitates structured performance reviews and goal tracking, promotes feedback culture in demanding legal environments, and tracks billable vs. non-billable performance for legal staff.

Some types include Lattice, 15Five, ClearCompany, and Trakstar.

Payroll and Compliance Software ensure accurate, on-time payroll, manages complex compensation models (e.g., partners vs. associates), maintains compliance with labor laws and bar association rules. Examples include Gusto, Paychex, Rippling, QuickBooks Payroll

Employee Engagement and Training Platforms support continuous legal education (CLE) and soft skills development, enhances employee satisfaction and retention, and offers DEI training critical in legal practice. Examples include Culture Amp, Coursera for Business, LinkedIn Learning, Litmos Technology & Criminal Defense (version 3)

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VIII. INVESTIGATION TECH In my opinion, technology’s purpose is to save time and create better products.

With regard to investigations, technology can shorten investigations, reveal leads that traditional methods miss, and find the facts that develop your theme and theory of the case. Always be mindful of the dangers using any investigation tool. Datasets that are traditionally used to train predictive tools can reflect bias as those sets may have been acquired through historically unfair policing patterns and socio-demographic bias. Garbage in, garbage out. The data sets are only as effective as the inputs. Outputs will always be limited by the inputs. Also, synthetic media will be used to frame people, create alibis, create fake evidence, etc. Innocent people will go to jail. AI is fully capable of fabricating audio/video or altering images. We used to say trust your eyes, ears and common sense. Now investigations must question the truth of what we are seeing or hearing on a recorded medium. Corroboration will be key. a. Helpful Investigative Technologies:

Technology & Criminal Defense (version 3)

demand error rates and validation. o CAINE Linux: a live Linux distro built for digital forensics, with many tools pre-installed. Good for initial analysis of drives / file systems. o Digital Forensic Labs: Lab services that are ISO 17025 certified (or equivalent) for digital evidence. eLab Forensics is one example, offering metadata tools, best practices, etc. o Drone/aerial imagery for scene reconstruction (comply with airspace, privacy, and Chapter 423). Timeline corroboration Page 52 of 63

How to tell if its Fake

o Advanced Data Aggregation and Link Analysis Tools: map phone records, social media, and financial flows to speed identification of relevant connections. o AI summarization for discovery review, call logs, and medical chronologies.

AI-Assisted Document Review and Timeline Generation: Medical Chronologies, Call Histories, etc. let investigators focus on strategy instead of rote sorting. o Bias in datasets used by facial recognition or predictive tools:

How to tell if its Fake

o OSINT platforms to collect and when properly documented and preserve social media, with hash legally acquired. and metadata capture. o Forensic Imaging Tools: Hard o Synthetic media and drive cloning / imaging with manipulated metadata; always cryptographic hashes, preserving seeking original files and full metadata; tools like EnCase, FTK provenance.

(Forensic Toolkit), Autopsy, o Write-blockers / Forensic X-Ways, Magnet, etc.

Bridges Prevent modification of o Link analysis/timeline tools source data when imaging drives, for CDRs, CSLI, and financial USBs, SD cards, etc. Essential for flows; geospatial overlays. preserving chain of custody. o Location-history and geofence Brands like Tableau, WiebeTech, data etc. — help ensure forensically o Mobile Device Forensic Tools: sound copies.

Tools that extract data from cell phones, tablets, e.g. software/hardware combos.

IX. COURTROOM TECH a. Jury Selection and Algorithmic Guidance AI-assisted jury-research products and sophisticated demographic analytics give trial teams more raw information about potential jurors. But the ABA and other ethics bodies have cautioned that counsel may run afoul of rules forbidding discrimination and that reliance on third-party analytics doesn’t eliminate lawyers’ ethical duties. Formal guidance now emphasizes that peremptory strikes cannot be used for discriminatory reasons even if a consultant or algorithm suggests them — and counsel must document independent legal judgment. Practically: vet any consultant/AI tool, preserve your work product, and be ready to explain the source and limits of any predictive model used in voir dire. b. Evidence and Demonstratives (including Virtual Reality)

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demonstrative device in at least one U.S. courtroom to let a judge “stand in” a defendant’s point of view; courts generally permit demonstratives but remain cautious about allowing VR or reconstructions as substantive evidence without reliable foundations. Counsel must be prepared to lay foundation, disclose methodology, and hire experts who can explain the assumptions behind a visualization. c. Remote Testimony & Witness Credibility

Remote testimony can increase access (especially for indigent clients or distant witnesses), but videoconference testimony changes nonverbal cues, raises reliability concerns (connectivity, coachable witnesses), and creates tactical opportunities for opposing counsel to manufacture “technical” credibility problems. Preparation, technical checks, and courtroom instruction requests are essential when a client or witness testifies remotely. d. Motions Practice

e. Authenticating and challenging digital evidence

Use self-authentication by certification where available; otherwise, lay foundation with a qualified witness. For audio/video, demand original files, device make/model, software versions, and chain-of-custody logs. For deepfake concerns, request pretrial authenticity hearings and propose limiting instructions.

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How to tell if its Fake

Admitting (or Excluding) Algorithmic or Synthetic Evidence: Expect more motions that challenge the authenticity of digital evidence, attacks on chain-ofcustody for electronically captured media, and Daubert/Giglio-style challenges to algorithmic tools (e.g., facial recognition, predictive scoring). Judges are beginning to require greater transparency about model training, error rates, and validation — so defense counsel should subpoena vendor documentation early and involve neutral experts when necessary. Use analytics ethically; document independent legal judgment for strikes; avoid discriminatory proxies. Prepare a short bench memo on any third-party analytics or AI used, with limitations and error rates.

f. Remote testimony & hybrid hearings Conduct tech checks (bandwidth, lighting, camera framing); prepare witness on remote demeanor. Request instructions against coaching and for disclosure of people present off camera. g. Demonstratives and VR

Disclose methodology and assumptions; retain experts who can explain error bars and limitations.

X.

CHAPTER 8 — PERILS OF TECH TODAY a. Tech Terrors

Automation complacency and overreliance: Blind trust in AI summaries or “confidence scores” without human review is dangerous. Always validate AIproduced outputs, and maintain human oversight and clear file notes showing proofreading and factual checks.

Generative AI moved from novelty to everyday legal work. Firms and vendors are shipping AI features that draft pleadings, summarize voluminous discovery, generate medical chronologies, and assist in legal research — and major practice-management vendors are investing heavily in AI product lines. These tools accelerate routine work but raise questions about accuracy, privilege, data handling, and supervision.

How to tell if its Fake

Biometrics: Fingerprint, iris, and voice recognition data as evidence.

Bomb disposal / EOD robots: Remotely operated ground robots to inspect, manipulate, or dispose of explosive devices; keep human officers at safe distance. The Dallas Police Department used a bomb-disposal robot in 2016 to deliver an explosive device to a suspect in a sniper situation (following a standoff) when no safe alternative was found. Many local sheriff’s and municipal departments have access to EOD robots (often via federal grants or surplus programs). Potential for misuse (e.g. lethal force with robots), public perception, needing robust guidelines, remote control and reliability, risk assessment of using explosives etc.

“Drone as First Responder” (DFR) programs.

Drones dispatched immediately to emergency 911 calls or urgent incidents; live feed, sometimes

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payload (e.g. Narcan delivery) to buy time before human responders arrive.

Laredo Police Department has expanded a state-funded Drone First Responder program: drones can reach certain calls in under 3 minutes, deliver Narcan in overdose cases, have thermal imaging and two-way audio. Edinburg PD is also launching a DFR program. Issues: cost of infrastructure (launch stations, trained operators), FAA rules, privacy, whether drones will be weaponized (most say no), reliability (weather, battery, signal)

Drones: Small quadcopters / multirotor drones: Relatively small, agile, can hover, carry cameras (HD, zoom, thermal) for surveillance, accident scene inspection, search & rescue. Texas Department of Public Safety (DPS) operates a large drone fleet, with many small/UAS aircraft. DPS has over 350 UAS, including many produced by DJI, Autel, etc. Flight time, battery life, line of sight, privacy concerns, regulatory compliance (FAA, Texas law)

Drones: VTOL / Long-range drones: Drones with longer endurance, vertical take-off and landing, can cover larger areas, useful for border patrol, wildfire mapping, etc. Texas DPS uses “long-range VTOL drones like the Dragonfish” among its fleet. Cost, weather performance, maintenance, airspace regulation Internet of Things (IoT) Evidence: Smart home devices (Alexa, Ring cameras, Fitbits) generating usable evidence.

Robotic “dogs” / quadruped robots: These are four-legged robots capable of moving over varied terrain, entering buildings, going up stairs, entering potentially dangerous spaces (e.g. search inside a house with a barricaded suspect). They often have cameras, sometimes manipulators, sometimes sensors for hazardous materials. Austin Police Department added Boston Dynamics’ “Spot” robot dog for the special operations / SWAT unit and hazardous materials detection.

Houston Police Department’s Tactical Technology & Criminal Defense (version 3)

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How to tell if its Fake

Opaque Algorithms and biased datasets: Opaque algorithms influencing investigations without transparency. When police tools (facial recognition, predictive policing) influence investigations, defense teams should demand transparency about training data, error rates, and vendor validation. Consider early discovery motions, Frye/Daubert challenges, and expert rebuttal.

Operations Division uses SPOT and another robot (“Hal”) to assist in high-risk scenes. Cost, maintenance, battery / operational endurance, ethical / privacy concerns (how they’re used, whether they collect data or surveil), risk of law enforcement setting precedents for more aggressive usage.

Robots/drones used for lethal force / controversial use Rare but notable case: the Dallas 2016 incident where police used a bomb-carrying robot to kill a suspect. It raised debates around precedent, legality, and oversight. Very rare; most use is non-lethal / reconnaissance / risk mitigation. However, that incident is a major example used in discussions of policy for regulation of robots. Ethical / constitutional issues, public opinion, potential for misuse, requirement for policy and oversight, transparency, ensuring use of force laws apply even when force is applied via a robot.

How to tell if its Fake

Robots: Ground reconnaissance / small throwable robots: Small robots that can be thrown or sent into tight spaces (e.g. underneath vehicles, into crawlspaces or around corners) to see what’s ahead, without sending in officers. Often used by SWAT or EOD. There’s less public detail about specific Texas cities beyond bomb disposal and large quadrupeds, but many federal/state/local agencies nationally (including in TX) have these via EOD/SWAT teams. Also, companies like ReconRobotics provide these (“Throwbot”, “Recon Scout”) to law enforcement agencies. Challenges: durability, maneuvering in cluttered or obstructed environments, ensuring signal integrity, ensuring operator training, cost vs benefit, legal / liability concerns Robots/Ground Unmanned Systems: In addition to aerial drones, Texas law enforcement also uses ground robots for specific tasks. These are especially useful in dangerous situations (bomb threat, SWAT standoffs, hazardous materials), for reconnaissance, or reducing risk to officers.

Surveillance / border monitoring: Large scale use for monitoring border crossings, wildlife cameras, license plate readers / AI for vehicle tracking, facial recognition, etc. Under Texas DPS’s “Operation Lone Star,” increased use of UAS, wildlife game cameras, AI-assisted image monitoring. Also, Texas has laws governing when unmanned aircraft may capture images. Civil liberties /

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privacy, oversight / transparency, legal limits (warrants, consent), risk of mission creep, reliability of AI detection, false positives etc.

Synthetic media and deepfakes are a present danger. Deepfakes and synthetic audio/video that can mislead factfinders. Courts, regulators, and legislatures are confronting cases in which audio/video were falsified or manipulated; at the same time, bad actors have used AI to create false audio recordings and pornographic images with real harm to victims. Audio or video that looks or sounds real can be created with little sophistication. Defense counsel should (a) insist on preservation and full metadata for any electronic evidence, (b) seek vendor/expert analysis of file provenance and manipulation artifacts, and (c) move for pretrial hearings to test authenticity. Recent highprofile incidents and prosecutions illustrate the real harms and legal consequences of synthetic media misuse. Judges and counsel must therefore treat media evidence with healthy skepticism and technical scrutiny.

Virtual Court and Remote Meetings: Remote depositions, hybrid hearings, and remote testimony remain tools courts grant under varying standards; evidence-presentation systems now must work equally well for jurors both inroom and online. That creates new advantages for client access and cost savings — and new tactical considerations for credibility and jury perception.

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How to tell if its Fake

Weaponized Surveillance and privacy intrusion: Drones, private-sector data aggregators, and ubiquitous cameras can produce huge volumes of surveillance data; analyze whether capture violated statutory or constitutional protections and challenge unlawfully obtained evidence. Texas’s statutory framework and recent litigation demonstrate the contested nature of drone surveillance. Texas Government Code 423 addresses use of unmanned aircraft (drones) including when law enforcement may legally capture images using them. Aerial devices used by law enforcement for surveillance, emergency response, border security, etc. AI surveillance tools, facial recognition, license plate readers (ALPRS), automatic monitoring via cameras (wildlife cameras, etc.) especially in the context of border security programs (e.g. Operation Lone Star). Texas’s drone laws and federal disputes over drone limits have produced

litigation and legislative attention; law enforcement drone use raises both investigative utility and privacy/exclusionary-rule issues.

Wearable Data: Apple Watch, Fitbit health data used in reconstructing timelines.

XI.

CHAPTER 9 — IS IT FAKE? DETERMINING AUTHENTICITY

See Additional Handout in Materials.

XII.

EXAMPLES OF LAW AND TECH COLLIDING

Example 1. Parent invades other Parent’s Privacy: Bio Dad records his son’s conversation with Bio Mom? Allen v. Mancini, 170 S.W.3d 167, 173 (Tex. App. 2005). “Common law allows a parent to vicariously consent to the recording through the child as a Joint Managing Conservator of the Child…”

Example 2. Parent invades other Parent’s Privacy: Bio Mom puts tracker on Bio Dad’s car to find out if Bio Dad is going to the bar after Bio Dad drops Child at soccer practice. In Texas, it is illegal to place a tracking device on another person’s vehicle without their consent. Texas Penal Code 16.06.

How to tell if its Fake

Example 3. Parent invades other Parent’s Privacy: Bio Mom puts tracker on Child after exchange with Bio Dad for his period of possession? In Texas, there is no statute prohibiting parents from tracking their child and parents are generally permitted such control.

Example 4. Accessing Another Person’s Device: A parent gathers incriminating evidence on other parent’s electronic devices including cell phones, tablets and computers? Depends on circumstances. Was permission or password obtained without the permission of the owner? Kelso v. State 562 S.W.3d 120, 135 (Tex. App. – Texarkana 2018, pet ref’d)

Example 5. Using Security Cameras: Can a wife hire a private investigator to set up cameras in the couple’s bedroom to record the bedroom while wife was out of town? Clayton v. Richards 47 S.W.3d 149, 153 (Tex. App. Texarkana 2001, Technology & Criminal Defense (version 3)

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pet. Denied). Placing an audio or video recording device requires the consent of at least one party where a party has an expectation of privacy despite the fact that he or she may share the space with another party like a wife and any video recording without consent infringed on his right to privacy.

Example 6. Attorney Immunity and Wiretapping. Taylor v. Tolbert, 644 S.W.3d 637 (Tex. 2022): Is Attorney Immunity a defense to wiretap statutes. Even though attorney immunity might apply to the Texas Wiretapping laws, it will not protect an attorney acting in the normal scope of representation if she violates the federal statute. Attorneys need to know the origin of evidence before using illegal and inadmissible intercepted communications to prevent civil and criminal liability not only for the client but for the attorney.

Example 7. AI MURDER VICTIM ELOCUTION: Using several AI tools, the murder victim’s family managed to create a convincing video using about a 4.5minute-video of Pelkey, his funeral photo and a script that the decedent’s sister prepared. They digitally removed the sunglasses on top of decedent’s hat and trimmed his beard — which had been causing technological issues. The video thanked everyone in Pelkey's life who contributed an impact statement and attended the hearing. Then, the video addressed his shooter, the Defendant. The video ended with the avatar encouraging everyone to love one another and live life to the fullest. "Well, I'm gonna go fishing now. Love you all. See you on the other side," it concluded. Neither the defense nor the judge pushed back. Later in the hearing, the Judge said, "I loved that AI. Thank you for that."19

19 https://www.npr.org/2025/05/07/g-s1-64640/ai-impact-statement-murder-victim

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How to tell if its Fake

Example 8. The US Court of Appeals for the 9th Circuit decided in 2024 that the Fifth Amendment protection against self-incrimination does not prohibit police officers from forcing a suspect to unlock a phone with a thumbprint scan. States courts have gone both ways on this issue, some claiming you cannot be compelled to use biometrics, but the US Supreme Court just denied certiorari

on this case, so it will remain unsettled for now. The Pin is mightier than the face. 20

Example 9. Officers were conducting a DUI enforcement operation when they spotted a Waymo robotaxi making an illegal U-turn in front of them at a light.

Officers pulled over the vehicle, which had no human driver behind the wheel, so they had to contact the company regarding the violation.21

How to tell if its Fake

Example 10. Prosper Police Department initiated a program to implement Drones as First Responders (DFR) version 2.0. Prosper’s DFR program combines the capability of drones to rapidly respond to a scene and relay valuable information to police personnel with a comprehensive citywide network of security cameras and license plate readers. Prosper adopted the Flock Aerodome DFR system developed by Flock Safety, a public safety software company. Prosper’s DFR system employs the FlockOS a real-time intelligence platform to integrate data from numerous sources, including video, license plate recognition (LPR), and audio to provide a comprehensive view of incidents.22 Arlington Police Department launched a similar DFR program after the Federal Aviation Administration named the Arlington Police Department as one of the first agencies in the nation to receive a waiver for Beyond Visual Line of Sight - allowing police drone pilots to fly further than unmanned aerial vehicles are typically permitted from inside the department’s real time crime center.23

Example 11. In Dallas, Texas, unable to subdue a suspect without compromising their safety, police decided to end a standoff by attaching explosives to a bomb robot and detonating them near the suspect, killing the 20 https://securityboulevard.com/2025/03/the-pin-is-mightier-than-the-face/;

https://www.zdnet.com/article/biometrics-vs-passcodes-what-lawyers-say-if-youre-worriedabout-warrantless-phone-searches/

21 https://www.wvlt.tv/2025/09/30/no-driver-no-clue-waymo-driverless-car-stopped-by-police-duringdui-operation/?outputType=amp 22 https://dronelife.com/2025/07/10/prosper-p-d-takes-public-safety-to-the-next-level-with-drone-firstresponder-2-0/amp/ 23 https://www.keranews.org/news/2025-05-08/arlington-police-use-drones-when-responding-to-somecalls-now-the-program-is-getting-an-expansion?_amp=true

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suspect and ending the armed conflict. This was a first in terms of use of a robot lethally by the police. 24

Appendix: PRACTICAL CHECKLISTS Before trial: Get vendor contracts and source code/validation docs if possible; subpoena logs, metadata, and raw files; retain a neutral forensic examiner.

During voir dire: Ask about jurors’ media use, familiarity with digital evidence, and exposure to social feeds; be cautious using AI recommendations to strike jurors.

During hearings/trial: Test demonstratives’ foundations; demand jury instructions explaining limitations of technological evidence when appropriate; preserve objections and create record.

Office policies: Adopt an AI-use policy, cybersecurity incident plan, vendor due-diligence checklist, and continuing education requirements for attorneys and staff. Map end-to-end workflows (intake → conflicts → engagement → discovery → trial/notices → billing → closure). Automate routine tasks (document assembly, reminders, status updates) with audit trails. Use secure client portals for messaging, payments, and discovery downloads.

24 https://www.pbs.org/newshour/science/robot-delivered-lethal-explosive-in-dallas-police-standoff-was-a-

first-experts-say

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How to tell if its Fake

Security Tips: Password manager + enforced multi-factor authentication (MFA) for all cloud services. Full-disk encryption on all devices; automatic screen lockouts; endpoint protection. Zero-trust mindset: least privilege access, role-based permissions for staff and vendors. Incident response plan and tested backups (3-2-1 rule). Adopt an internal AI-use policy (confidentiality, verification, logging). Keep client data in tools that offer enterprise controls and no training-on-your-data by default. Always human-review AI outputs; never cite uncited AI statements.

Courtroom Scouting Report: o explain limits of digital evidence. o have offline copies of exhibits. o preserve objections and offers of proof. o Test Equipment: o Computer o Charger o USB Hub o Clicker o External Hardrive(s) o USB(s) o Hotspot o Phone Charger o Power bank o Adaptors o Cables

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How to tell if its Fake

o Screens/Monitors o Audio o Connectivity o Document Display/Projection o Other Capabilities o Potential Obstacles o A/V Controller o Internet/WiFi Info (Network & Password) o Diagram Layout to the physical Courtroom. o Before trial: o subpoena metadata, logs, and vendor docs. o retain a neutral forensic examiner. o During voir dire: o ask about media literacy and social media.

AI & Anatomy of a Trial September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: Ethics of AI

Speaker:

Brent Mayr

5300 Memorial Dr Ste 750 Houston, TX 77007-8228 713.808.9613 phone bmayr@mayr-law.com email http://www.mayr-law.com/ website

Ethics of AI

Texas Criminal Defense Lawyers Association

Ethics of AI

ETHICS OF ARTIFICIAL INTELLIGENCE: A Guide for Texas Criminal Defense Lawyers Brent Mayr Managing Shareholder, Mayr Law, P.C. | Co-Chair, TCDLA Ethics Committee TCDLA AI & Anatomy of a Trial | September 3–4, 2026 | The Woodlands Resort

I. Introduction Artificial intelligence has arrived in our daily lives as criminal defense lawyers, and it is not leaving.

Prosecutors already use AI to organize evidence, spot patterns in digital data, link cases together, and draft charging documents. Courts are writing rules to govern AI use. Bar authorities are issuing guidance. And a growing body of sanctions decisions proves that careless or dishonest AI use carries real professional consequences — including, in the most recent cases, suspension from practice before a federal appellate court and referral to state disciplinary authorities.

This article tells Texas criminal defense lawyers where the law stands today, what their ethical obligations require, and how to use these powerful tools responsibly on behalf of clients whose liberty depends on them. It draws on the Texas Disciplinary Rules of Professional Conduct, Texas Professional Ethics Committee Opinion 705, and the growing body of national authority that bears on our practice. The questions are practical: What must I understand before using AI? What may I safely input into an AI tool, and what must I keep out? What must I verify before filing?

What must my clients understand about their own AI use? And how do I stay current in a landscape that changes faster than any single publication can track?

That last question is one reason TCDLA built an entire seminar around this topic. This article reflects the state of the law as of July 2026. New ethics opinions will follow. Courts will keep sharpening sanctions doctrine. But the duties of competence, confidentiality, candor, and supervision — the foundational duties AI stress-tests — will stay constant even as the specific guidance evolves. Master those foundational duties, and you have already done most of the work.

II. The Texas Ethical Framework: Opinion 705 and the Disciplinary Rules A. The Foundation: Texas Professional Ethics Opinion 705 In February 2025, the Professional Ethics Committee of the State Bar of Texas issued Opinion 705 — Texas’s first formal guidance on generative AI in legal practice. 1 The Taskforce for Responsible 1 Tex. Comm. on Professional Ethics, Op. 705 (Feb. 2025), available at https://www.legalethicstexas.com/resources/opinions/

opinion-705/ .

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

B. Competence: Rule 1.01 and the Duty of Technological Literacy AI use triggers, above all, the duty of competence. Rule 1.01(a) of the TDRPC defines “competence” as the legal knowledge, skill, and training reasonably necessary for the representation.

Comment 8 requires lawyers to “strive to become and remain proficient and competent in the practice of law, including the benefits and risks associated with relevant technology.” Opinion 705 applies this standard directly: a lawyer who uses a generative AI tool must have “a reasonable and current understanding of the technology.” 4 This means understanding the risk of hallucinations — the well-documented tendency of large language models to generate plausible-sounding but entirely fabricated content — as well as the specific capabilities and weaknesses of the particular tool being used.

The stakes are not abstract. Researchers at Stanford’s RegLab and Institute for Human-Centered Artificial Intelligence studied leading legal AI platforms and found alarming error rates: Lexis+ AI produced incorrect information more than 17% of the time; Westlaw’s AI-Assisted Research hallucinated more than 33% of the time. General-purpose large language models performed worse still. 5 A lawyer who uses any of these tools without understanding these limitations — and without implementing independent verification as a matter of routine — does not satisfy the competence standard under Rule 1.01.

The National Association of Criminal Defense Lawyers’ 2026 white paper, Parity in Practice: The Defender’s Duty to Ethically Use AI, adds a dimension to competence that Texas criminal defense

2 State Bar of Texas, Taskforce for Responsible AI in the Law (TRAIL), Report on Artificial Intelligence and the Practice of Law

(2024); State Bar of Texas, AI Toolkit, Texas Bar Practice, available at https://www.texasbarpractice.com/artificial-intelligencetoolkit/.

3 Op. 705 at 1, citing Professional Ethics Committee Op. 680 (Sept. 2018) (cloud computing) and Op. 665 (Dec. 2016) (metadata

in electronic documents).

4 Tex. Disciplinary Rules of Prof’l Conduct R. 1.01(a); Preamble, Terminology (defining ‘competence’); Op. 705 at 2 (a lawyer who

uses generative AI must have a reasonable and current understanding of the technology).

5 Varun Magesh, Faiz Surani, Matthew Dahl, Mirac Suzgun, Christopher D. Manning & Daniel E. Ho, Hallucination-Free? Assessing

the Reliability of Leading AI Legal Research Tools, 22 J. Empirical Legal Stud. 216 (2025) (finding Lexis+ AI incorrect more than 17% of the time and Westlaw AI-Assisted Research hallucinating more than 34% of the time; general-purpose LLMs fared worse still).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Ethics of AI

AI in the Law (TRAIL) requested the opinion and also produced the State Bar’s AI Toolkit — the first toolkit of its kind in the country, according to the Bar. 2 Opinion 705 creates no new ethical obligations. It maps the existing Texas Disciplinary Rules of Professional Conduct (TDRPC) onto a technology most practitioners are still learning, framing the exercise as a continuation of prior guidance on cloud computing and electronic metadata. 3 The technology is new. The ethical framework is not. What has changed is the stakes.

Ethics of AI

lawyers must take seriously. 6 Competence means more than understanding AI’s limits as a drafting tool. It requires understanding how prosecutors use AI. The Vanderbilt Project on Prosecution Policy has documented prosecutorial AI use for DNA analysis, digital evidence extraction, automated transcription, redaction, and pattern identification across cases. 7 A defense lawyer who cannot recognize when to challenge these tools, or when to demand discovery about their use, may fail the competence standard in a way that directly harms the client.

C. Confidentiality: Rule 1.05 and the Data Exposure Problem Rule 1.05 of the TDRPC imposes a broad duty of confidentiality covering not just privileged communications but all information relating to a client or furnished by the client during the representation. Opinion 705 treats confidentiality as one of generative AI’s most significant ethical challenges: many AI tools, by design and by their terms of service, retain user inputs and may feed them into future training data.8 The Texas Data Privacy and Security Act (TDPSA), effective July 2024, adds a statutory layer here. 9 1. Open vs. Closed AI Systems: A Critical Distinction The NACDL white paper offers an indispensable framework for AI confidentiality obligations: the distinction between open (consumer-facing) AI systems and closed (enterprise) AI systems.10 Open systems — which include the free and low-cost tiers of ChatGPT, Claude, Gemini, Grok, and similar platforms — may retain user inputs, use them to train future model versions, and make them accessible to third parties, including in some circumstances law enforcement. Closed or enterprise systems operate within private, contractually governed environments that restrict data from leaving the organization’s control and typically include explicit prohibitions on using client data for model training.

The distinction matters enormously for criminal defense practice. A lawyer who inputs client facts — the specific facts of the alleged offense, the client’s prior criminal history, mental health information, immigration status, or defense strategy — into a consumer-tier AI platform may be ex-

6 NACDL, Parity in Practice: The Defender’s Duty to Ethically Use AI (2026), available at https://www.nacdl.org/Document/ Par-

ityinPracticeDefenderAIUse.

7 Alissa Marque Heydari, AI & Prosecution: Mapping the Current and Future Roles of Artificial Intelligence in Prosecution, Van-

derbilt Project on Prosecution Policy & Policing Project at N.Y.U. Sch. of Law (Dec. 2024) (documenting prosecutorial AI use for DNA analysis, digital evidence extraction, automated transcription, redaction, and pattern identification across cases).

8 Tex. Disciplinary Rules of Prof’l Conduct R. 1.05; Op. 705 at 3–4 (discussing confidentiality risks of inputting client information

into generative AI tools).

9 Texas Data Privacy and Security Act (TDPSA), Tex. Bus. & Com. Code §§ 541.001 et seq. (effective July 1, 2024).

10 NACDL, Parity in Practice at 13—15 (distinguishing open/consumer-facing AI systems from closed/enterprise systems with

contractual confidentiality guarantees); Op. 705 at 3 (noting concern that self-learning programs may store and reveal confidential information to third parties).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

2. The Privilege Problem: United States v. Heppner The data exposure risk moved from theoretical to concrete in February 2026. The United States District Court for the Southern District of New York decided, on a question of first impression nationwide, that a criminal defendant’s written exchanges with Claude enjoyed neither the attorney-client privilege nor work product protection. 12 The facts illustrate the problem with clarity. After being indicted on federal securities and wire fraud charges, defendant Bradley Heppner used the consumer version of Anthropic’s Claude — the publicly available tier, not an enterprise account — to generate written analyses of his defense strategy. He did this on his own initiative, without being directed to do so by his lawyers.

When the government executed a search warrant at his residence, it seized electronic devices containing these AI-generated documents and moved to use them at trial. Judge Rakoff agreed: because Claude is not an attorney, the communications could not satisfy the attorney-client privilege’s threshold requirement; 13 and because the consumer version’s privacy policy permits use of inputs for training and model improvement, Heppner had no reasonable expectation of confidentiality.

A federal court in Michigan, ruling a week prior, however, reached a different result on different facts. In Warner v. Gilbarco, Inc., the court held that a pro se civil litigant’s ChatGPT-assisted filings were protected work product, reasoning that AI is a “tool, not a person” and that disclosing information to a tool differs from disclosing it to an adversary. 14 The Heppner/Warner split leaves important questions unsettled. But one point is settled: a client who types defense strategy into a consumer AI platform may create a document the government can seize and read. “Don’t talk to anyone about your case” must now include “that includes the chatbot on your phone.” Raise this explicitly in your first client meeting and in your written engagement communications.

11 Op. 705 at 4, drawing on Op. 680 (reasonable precautions for cloud-based technology: understand the technology, review

vendor terms of service, investigate data-security protections, and train all lawyers and staff).

12 United States v. Heppner, No. 1:25-cr-00503-JSR, 2026 WL 436479 (S.D.N.Y. Feb. 17, 2026) (Rakoff, J.) (ruling on question of

first impression nationwide that a criminal defendant’s written exchanges with the consumer version of Anthropic’s Claude were not protected by attorney-client privilege or the work product doctrine).

13 Id. at *3 (“Because Claude is not an attorney, that alone disposes of Heppner’s claim of privilege”).

14 Warner v. Gilbarco, Inc., No. 2:24-CV-12333, 2026 WL 373043, at *4 (E.D. Mich. Feb. 10, 2026) (Patti, M.J.) (holding that a pro

se civil litigant’s ChatGPT-assisted filings were protected work product, reasoning that generative AI programs “are tools, not persons,” and that disclosing information to a tool is not the same as disclosing it to an adversary).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

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posing that information to third parties in violation of Rule 1.05. Opinion 705 calls for a framework of reasonable precautions drawn from prior cloud-computing guidance: understand how the tool works; review the vendor’s terms of service; investigate data-security protections; and train all lawyers and staff on appropriate use.11

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D. Supervision: Rules 5.01 and 5.03 and the Non-Delegable Duty to Verify Opinion 705 speaks most emphatically on supervision. The Committee states plainly that “lawyers are responsible for the work product they submit regardless of who (or what) does the original research and drafting.” 15 The opinion draws an explicit analogy to Rule 5.03’s supervision of nonlawyer assistants: a lawyer who relies on AI for research, drafting, and communication without independent verification runs the same risks as one who relies on an inexperienced or overconfident assistant.16 This duty extends beyond the individual lawyer. Defense lawyers who employ paralegals, law clerks, associates, or legal assistants must adopt clear policies governing everyone’s AI use. A paralegal who uses AI to draft a motion to suppress creates liability for the supervising attorney if the court receives that work without adequate review. You cannot delegate the duty to verify.

The NACDL white paper offers a useful frame: treat AI output as a first draft from a confident but unreliable assistant who never attended law school, bears no professional accountability, and has no idea whether the cases it cited even exist. Confirm every citation in Westlaw, Lexis, or a comparable authenticated database. Verify every legal proposition against the actual holding.

Check every factual assertion against the record.

E. Candor: Rule 3.03 and the Prohibition on False Statements to a Tribunal Rule 3.03 of the TDRPC prohibits lawyers from making false statements of material fact or law to a tribunal. 17 This is perhaps the most critically implicated ethical obligation in the context of AI hallucinations: a lawyer who submits a brief citing a non-existent case has made a false statement of law to the tribunal, regardless of whether the lawyer knew the citation was fabricated. The duty of reasonable inquiry — the obligation to verify the accuracy of legal assertions before submitting them to a court — is not discharged by saying the AI told me it existed. Federal courts have uniformly rejected this defense to Rule 11 sanctions.

F. Fees: Rule 1.04 and the Economics of AI Efficiency Generative AI can dramatically compress the time required for legal research, brief drafting, and document review. Opinion 705 is clear on the billing implications: a lawyer may charge for actual time spent using a generative AI tool, including time spent refining prompts, reviewing outputs, 15 Tex. Disciplinary Rules of Prof’l Conduct R. 5.01, 5.03; Op. 705 at 5 (lawyers cannot blindly rely upon or use answers given by

generative AI tools and remain responsible for the resulting work product regardless of who or what produced the initial draft).

16 Op. 705 at 5 (“lawyers are responsible for the work product they submit regardless of who (or what) does the original research

and drafting”).

17 Tex. Disciplinary Rules of Prof’l Conduct R. 3.03(a)(1) (prohibiting false statements of material fact or law to a tribunal); Op.

705 at 5 (connecting candor obligations to AI-generated content).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

III. National Guidance: ABA Formal Opinion 512 and Persuasive Authority A. ABA Formal Opinion 512 (July 2024) On July 29, 2024, the American Bar Association Standing Committee on Ethics and Professional Responsibility issued Formal Opinion 512 — its first comprehensive ethics opinion on generative AI. 19 Applying the Model Rules of Professional Conduct, Opinion 512 addresses competence, confidentiality, client communication, candor toward the tribunal, supervisory duties, and reasonable fees.

On competence, Opinion 512 requires lawyers to maintain “a reasonable understanding of the capabilities and limitations of any AI tool they employ”—an understanding that must be updated as the technology evolves. 20 On client communication, Opinion 512 goes further than Texas’s Opinion 705: the ABA concludes that Model Rule 1.4 requires disclosure and, in many circumstances, informed consent before a lawyer uses confidential information with an AI tool. 21 Texas Opinion 705 uses softer language: lawyers should “consider informing clients” and “may need to secure client consent.” 22 B. ABA Task Force Year 2 Report (December 2025) The ABA Task Force on Law and Artificial Intelligence released its Year 2 Report on December 15, 2025.23 Its central finding is sobering: “our profession has reached a crossroads: AI adoption has 18 Tex. Disciplinary Rules of Prof’l Conduct R. 1.04; Op. 705 at 6–7 (a lawyer may charge for actual time spent using a generative

AI tool, including time refining prompts, reviewing outputs, and verifying results, but may not charge for time saved by the technology); D.C. Legal Ethics Op. 388 (2024).

19ABA Comm. on Ethics & Prof’l Responsibility, Formal Op. 512 (July 29, 2024) (Generative Artificial Intelligence Tools).

20ABA Formal Op. 512 at 2–3 (citing Model Rule 1.1 and cmt. [8]).

21ABA Formal Op. 512 at 6–8 (discussing Model Rule 1.4 and the circumstances under which informed client consent is required

before confidential information is used in conjunction with an AI tool).

22Op. 705 at 4 (“lawyers should consider informing clients about the associated risks” and “may need to secure client consent”

when confidential information will be involved).

23ABA Task Force on Law and Artificial Intelligence, Addressing the Legal Challenges of AI: Year 2 Report on the Impact of AI on

the Practice of Law (Dec. 15, 2025), available at https://www.americanbar.org/groups/leadership/office_of_the_president/aitask-force/ (finding that “our profession has reached a crossroads: AI adoption has surpassed understanding”).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

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and verifying results. A lawyer may not charge for time “saved” by the AI program. 18 For criminal defense lawyers working on flat fees or court appointments, the question is different but equally significant: when AI compresses an eight-hour task to two hours, where does the savings go?

Opinion 705 suggests the answer is clear — the efficiency savings must benefit the client. Lawyers may not pass AI subscription costs to clients as disbursements unless the tool is a standalone expense for a specific client matter and the client has been informed.

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surpassed understanding.” AI has moved from experiment to core infrastructure for legal practice, courts, and legal education — but the profession’s collective understanding has not kept pace. The Task Force also drafted guidelines for federal judicial officers on responsible AI use, offering an early window into how courts will treat AI-assisted work product.

C. Other Jurisdictions as Persuasive Authority The TDRPC and Opinion 705 govern Texas practitioners, but other state bars’ ethics opinions carry persuasive weight and show where the law is heading nationally. California’s proposed rule amendments of March 2026 would, if adopted, make that state’s verification requirement for AIgenerated citations an enforceable disciplinary rule — the first of its kind in the country. 24 Florida Bar Ethics Opinion 24-1 (2024) concluded that AI-driven efficiencies must not result in inflated billing and that informed client consent is required before using AI with confidential information.25 The New York State Bar Association’s 2024 Task Force Report provided detailed guidance on AI vendor contracting and the use of AI in high-stakes litigation. 26 These opinions do not bind Texas lawyers, but they show the emerging national consensus and will likely inform future Texas guidance.

IV. The Sanctions Landscape: A Rapidly Escalating Consequence A. The Hallucination Problem AI hallucinations — plausible-sounding but wholly fictitious information — have produced a growing body of sanctions decisions that track AI adoption’s growth in legal practice. The Stanford RegLab’s hallucination study and Damien Charlotin’s AI Hallucination Cases Database, which now tracks well over a thousand cases and growing, document the scope of the problem.27 Every one of these cases teaches the same lesson: relying on AI does not discharge the duty to verify, and “I relied on AI” is no defense to sanctions for filing a brief that cites a case that does not exist.

24 Cal. Standing Comm. on Prof’l Responsibility & Conduct (COPRAC), Proposed Amendments to California Rules of Professional

Conduct (Mar. 13, 2026) (opening proposed amendments to six Rules of Professional Conduct for public comment, including a proposed Comment 3 to the candor rule requiring verification of all cited authorities, including those generated by AI, before submission to a tribunal).

25 Fla. Bar Ethics Op. 24-1 (2024) (concluding that AI-driven efficiencies must not result in falsely inflated billing and that informed

client consent is required before using AI with confidential information).

26 New York State Bar Ass’n, Report and Recommendations of the Task Force on Artificial Intelligence (Apr. 6, 2024) (addressing

competence, confidentiality, disclosure, and oversight, with particular attention to AI vendor contracting and the use of AI in high-stakes litigation including criminal defense).

27 Magesh et al., supra note 5; Damien Charlotin, AI Hallucination Cases Database (2026), https://www.damiencharlotin.com/hal-

lucinations/ (tracking nearly 1,600 cases globally involving AI-related citation errors as of July 2026).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Sanctions have escalated sharply in a short period. Courts do not treat AI errors as novel technical mishaps deserving special leniency; they treat them as ordinary violations of candor and competence, and they address them with ordinary — and now substantial — disciplinary tools. 

Mata v. Avianca, Inc., No. 22-cv-1461, 678 F. Supp. 3d 443 (S.D.N.Y. 2023): The foundational case. Attorneys submitted a brief citing six non-existent judicial opinions fabricated by ChatGPT. When the court questioned the citations, the attorneys doubled down, submitting affidavits and additional correspondence that cited further fictitious cases. Sanction: $5,000. 28



Gauthier v. Goodyear Tire & Rubber Co., No. 1:23-cv-281, 2024 WL 4882651 (E.D. Tex. Nov. 25, 2024): A Beaumont, Texas attorney submitted a summary judgment response with two non-existent cases and multiple fabricated quotations generated by an AI tool. No corrective action was taken until the court issued a show-cause order. Sanction: $2,000 and mandatory CLE on AI use. The Eastern District’s Local Rule AT-3(m) already explicitly warned attorneys that AI tools “may produce factually or legally inaccurate content and should never replace the lawyer’s most important asset—the exercise of independent legal judgment.” 29



Kaur v. Desso, No. 9:25-cv-00726, 2025 WL 1895859 (N.D.N.Y. July 9, 2025): An attorney used Anthropic’s Claude Sonnet 4 to draft a supplemental brief in an emergency habeas matter seeking to halt a client’s deportation, producing fabricated quotations from cited authorities.

The attorney admitted knowing AI tools hallucinate but felt pressured by an expedited timeline. Sanction: $1,000 and mandatory CLE (reduced in light of prompt admission and withdrawal of the filing). Key holding: “An attorney who acts with ‘an empty head and a pure heart’ is nonetheless responsible.” 30



Mid Central Operating Engineers Health & Welfare Fund v. HoosierVac LLC, No. 2:24-CV00326-JPH-MJD, 2025 WL 574234 (S.D. Ind. 2025): A Texas attorney filed three separate briefs in an ERISA case with AI-fabricated citations, admitted he did not verify any of them, and

28 Mata v. Avianca, Inc., No. 22-cv-1461, 678 F. Supp. 3d 443 (S.D.N.Y. 2023) (Castel, J.) (imposing $5,000 sanction after attorneys

submitted six non-existent judicial opinions fabricated by ChatGPT and then doubled down when the court questioned the citations, submitting affidavits and additional correspondence that cited further fictitious cases).

29 Gauthier v. Goodyear Tire & Rubber Co., No. 1:23-cv-281, 2024 WL 4882651 (E.D. Tex. Nov. 25, 2024) (Crone, J.) (imposing

$2,000 sanction and mandatory CLE after a Beaumont, Texas attorney submitted a summary judgment response containing AIfabricated citations and took no corrective action until the court issued a show-cause order); E.D. Tex. Local Rule AT-3(m), Standards of Practice to be Observed by Attorneys (cautioning that AI tools “may produce factually or legally inaccurate content and should never replace the lawyer’s most important asset—the exercise of independent legal judgment”).

30 Kaur v. Desso, No. 9:25-cv-00726, 2025 WL 1895859 (N.D.N.Y. July 9, 2025) (Nardacci, J.) (imposing $1,000 sanction after

attorney used Anthropic’s Claude Sonnet 4 to draft a supplemental habeas brief containing fabricated quotations, knowing that AI tools hallucinate but feeling pressured by an expedited timeline; sanction reduced from typical range in light of attorney’s prompt admission and withdrawal of the filing).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

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B. A Progression of Consequences

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apologized to the court. Recommended sanction: $15,000—substantially higher than prior cases, reflecting the pattern of repeated misconduct across multiple filings. The magistrate judge wrote that “the use of artificial intelligence must be accompanied by the application of actual intelligence in its execution.” 31 

Billups v. Louisville Municipal School District, No. 1:24-cv-00074, 2025 WL 3691871 (N.D. Miss.

Dec. 19, 2025): A junior attorney used Grok (xAI’s chatbot) to draft a summary-judgment response, producing one entirely fabricated citation and three that misrepresented the holdings of real cases—in violation of her own firm’s internal AI policy. At the sanctions hearing: “I made a big mistake. I was lazy. I did not check my work.” Sanctions: disqualification from the case; mandatory distribution of the order to every judge in every active case; a two-year bar on appearing in new cases before the sanctioning judge; and an internal audit of prior filings. The court wrote: “There’s a difference between a seasoned, good lawyer and somebody who uses AI to look like one.” 32



Fletcher v. Experian Information Solutions, Inc., No. 25-20086, 2026 WL 456842 (5th Cir. Feb. 18, 2026) (unpublished): The Fifth Circuit sanctioned appellate counsel $2,500 under FRAP 46(c) and the court’s inherent authority after a reply brief contained sixteen fabricated quotations and five additional material misrepresentations of law or fact, drafted substantially by generative AI and not independently verified. The court confirmed that existing rules are adequate to address AI misconduct and that no special AI-certification rule is needed.33



Lnu v. Blanche, No. 24-4790, 2026 WL 1587554 (9th Cir. June 3, 2026): Two Orange County immigration attorneys were suspended from practice before the Ninth Circuit for six months, fined $2,500 each, and referred to the California State Bar, after submitting briefs with fabricated citations and then characterizing the errors as “innocent typographical mistakes” while denying AI use until effectively the final moments of oral argument. For two years, all filings

31 Mid Cent. Operating Eng’rs Health & Welfare Fund v. HoosierVac LLC, No. 2:24-CV-00326-JPH-MJD, 2025 WL 574234 (S.D. Ind.

2025) (Dinsmore, M.J.) (recommending $15,000 sanction against a Rio Hondo, Texas attorney for filing three separate briefs with AI-fabricated citations in an ERISA case without conducting any independent verification; the escalated sanction reflected the pattern of repeated conduct across multiple filings).

32 Billups v. Louisville Mun. Sch. Dist., No. 1:24-cv-00074, 2025 WL 3691871 (N.D. Miss. Dec. 19, 2025) (Aycock, J.) (sanctioning a

junior attorney who used Grok (xAI’s chatbot) to draft a summary-judgment response in an age-discrimination case, producing one entirely fabricated citation and three that misrepresented actual holdings, in violation of the firm’s own internal AI policy; sanctions included disqualification from the case, mandatory distribution of the order to all active-case judges, a two-year bar on appearing in new cases before the sanctioning judge, and an internal audit of prior filings).

33 Fletcher v. Experian Information Solutions, Inc., No. 25-20086, 2026 WL 456842 (5th Cir. Feb. 18, 2026) (Elrod, C.J.) (un-

published; not designated for publication under 5th Cir. R. 47.5 and therefore not binding precedent, though persuasive) (sanctioning appellate counsel $2,500 under FRAP 46(c) and the court’s inherent authority after a reply brief contained sixteen fabricated quotations and five additional material misrepresentations of law or fact, drafted substantially by generative AI and not independently verified).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

C. The Cover-Up Multiplier The climb from $5,000 (Mata, 2023) to a six-month appellate suspension with a bar referral (Lnu, 2026) reflects one consistent aggravating factor: the cover-up. In nearly every high-sanction case, the attorney’s first response to the court’s questions was denial, deflection, or an implausible explanation. The Fifth Circuit in Fletcher was explicit: “Had Hersh accepted responsibility and been more forthcoming, it is likely that the court would have imposed lesser sanctions.” 35 Candor at the first sign of trouble—prompt disclosure, withdrawal of the defective filing, a transparent explanation—does more to mitigate sanctions than anything else. It is also the right thing to do.

D. Court-Specific Disclosure Requirements in Texas Federal Courts Texas criminal defense lawyers in federal court face disclosure requirements that vary by district.

Northern District of Texas—Local Civil Rule 7.2(f) (effective September 2, 2025) states: “Any brief prepared using generative artificial intelligence must disclose that fact on the first page of the filing; a brief without that disclosure certifies that no generative AI was used.” 36 Judge Brantley Starr’s earlier standing order pioneered this approach by requiring attorneys to certify either that no portion of the filing relied on generative AI, or that any AI-generated language had been independently verified. 37 Southern District of Texas—General Order 2025-04 (May 7, 2025): Cautions that AI “may produce factually or legally inaccurate content and should never replace the lawyer’s independent legal judgment,” with Rule 11 sanctions available for AI-related submissions. 38

34 Lnu v. Blanche, No. 24-4790, 2026 WL 1587554 (9th Cir. June 3, 2026) (suspending two Orange County immigration attorneys

from practice before the Ninth Circuit for six months, fining each $2,500, and referring the matter to the California State Bar, after counsel submitted briefs with fabricated citations and characterized the errors as “innocent typographical mistakes” while denying AI use until effectively the final moments of oral argument).

35 Fletcher, 2026 WL 456842 (“Had Hersh accepted responsibility and been more forthcoming, it is likely that the court would

have imposed lesser sanctions”).

36 N.D. Tex. Local Civil Rule 7.2(f) (effective Sept. 2, 2025) (requiring that any brief prepared using generative artificial intelligence

disclose that fact on the first page of the filing; a brief that does not contain the disclosure is deemed to certify that no generative AI was used in its preparation).

37 Judge Brantley Starr, N.D. Tex., Mandatory Certification Regarding Generative Artificial Intelligence (2023) (requiring attorneys to certify either that no portion of the filing relied on generative AI, or that any AI-generated language had been independently verified through traditional legal research tools).

38 S.D. Tex. General Order 2025-04 (May 7, 2025) (Crane, C.J.) (cautioning that AI tools “may produce factually or legally inaccu-

rate content and should never replace the lawyer’s independent legal judgment” and confirming that Rule 11 sanctions remain available for AI-related submissions).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

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by their firm must include a sworn statement disclosing AI use, naming the tool, and certifying that the signing attorney personally reviewed every citation and quotation. 34

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Eastern District of Texas—General Order 25-07 (October 31, 2025): Extends AI-verification obligations to all parties.39 Texas state courts have not adopted uniform AI disclosure requirements.

Monitor the Texas Office of Court Administration and individual courts for new standing orders.

The Texas Responsible AI Governance Act (TRAIGA), enacted June 2025, adds a statutory layer relevant to AI use in professional services. 40

V. The Defender’s Affirmative Duty: Parity in Practice A. The Adversarial Argument for Engaging with AI The ethics of AI in criminal defense is not only about what lawyers must not do. The NACDL’s Parity in Practice makes a compelling argument: criminal defense lawyers may have an ethical duty not merely to use AI responsibly but to engage with it. 41 The argument rests on the adversarial nature of the system itself. Prosecutors already use enterprise-level AI for evidence management, automated transcription, digital data extraction, and pattern identification across cases. When one side gains a meaningful technological advantage, it disrupts the adversarial balance that effective representation requires.

This argument rests on familiar doctrine. Rule 1.01’s competence standard requires understanding what AI tools exist, how prosecutors use them, when to challenge them, and whether comparable tools might help the client. This does not mean every defense lawyer must adopt every available AI tool. It means a lawyer must know what exists, assess whether it would help a given client, and recognize when prosecutorial AI use warrants challenge or a Daubert-style inquiry.

B. Specific Criminal Defense Applications The NACDL white paper catalogs AI applications already improving criminal defense practice. 42 Evidence and Discovery Review: Public defender offices use AI-powered transcription and analysis tools such as JusticeText and Reduct to accelerate review of body-worn camera footage, interro-

39 E.D. Tex. General Order 25-07 (Oct. 31, 2025) (Mazzant, C.J.) (amending Local Rule CV-11(g) to extend AI-verification obliga-

tions beyond pro se litigants to all parties appearing before the court).

40 Texas Responsible AI Governance Act (TRAIGA) (enacted June 2025) (establishing AI transparency, accountability, and human-

oversight obligations that touch professional services including legal practice).

41 NACDL, Parity in Practice at 9, 20–22 (2026) (arguing that defenders have an ethical obligation rooted in the duty of competence under Model Rule 1.1 and the duty of diligence under Model Rule 1.3 to understand what AI tools exist, how prosecutors use them, when to object, and whether comparable tools might benefit their own clients).

42 NACDL, Parity in Practice at 16–20 (2026) (cataloging AI applications in criminal defense including JusticeText, Reduct, and

similar tools; noting that the Kentucky Department of Public Advocacy reported JusticeText “reduced time spent reviewing evidence by hours per case, allowing them to find key contradictions in police statements more efficiently”).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Legal Research and Motion Drafting: AI research tools identify relevant precedents across jurisdictions faster than manual research alone, generate first-draft arguments for a lawyer to refine and verify, and suggest counterarguments to anticipated government positions. But the Stanford hallucination study shows that even legal-specific AI tools produce incorrect results at meaningful rates. Treat AI research as a starting point, never an ending point. Confirm every AI-generated citation in an authenticated database before it appears in any filing.

Sentencing Advocacy: AI platforms can analyze federal sentencing data and generate statistical reports on how similarly situated defendants have been sentenced, giving empirical support to variance arguments. AI can also help family members and supporters structure character letters effectively. The NACDL white paper flags a real risk, though: AI-generated character letters from multiple supporters may share suspiciously similar phrasing, signaling to a judge that the letters were manufactured rather than genuine. Judges increasingly spot AI-generated content. 43 Challenging Government AI Use: Perhaps the most underappreciated payoff of AI competence is the ability to challenge the AI tools the government uses against clients. Facial recognition evidence, risk assessment instruments, predictive policing tools, and AI-assisted DNA analysis all raise serious reliability, bias, and due process concerns. A defender who lacks AI literacy cannot effectively challenge these tools — cannot ask the right questions about error rates, training data, and algorithmic bias, and cannot recognize when expert testimony on AI reliability is warranted.

C. The Skill Development Concern The NACDL white paper flags a risk that cuts the other way: “skill flattening.” Criminal defense demands capabilities AI cannot replicate — reading a witness in real time, responding to unexpected testimony, adjusting strategy to a judge’s reactions, and advocating with the authenticity effective defense requires. Over-reliance on AI for drafting and research, especially by early-ca-

43 NACDL, Parity in Practice at 18–19 (2026) (discussing sentencing advocacy applications, noting that AI-generated character

letters from multiple supporters may share suspiciously similar phrasing and that “judges are increasingly adept at recognizing AI-generated content”).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

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gation recordings, and jail calls. The Kentucky Department of Public Advocacy reported that JusticeText “reduced time spent reviewing evidence by hours per case, allowing them to find key contradictions in police statements more efficiently.” One Wisconsin defender used JusticeText to uncover police misconduct buried in forty hours of footage that would otherwise have demanded substantial manual review. Texas criminal defense lawyers now face voluminous digital discovery as a matter of routine in serious felony cases. AI transcription and analysis tools can mean the difference between adequate preparation and trial by exhaustion.

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reer lawyers who have not yet built the analytical instincts that manual work develops, can atrophy the skills advocacy ultimately requires. AI should supplement human judgment, never replace it.44 D. The Two-Tiered Justice Problem The NACDL white paper raises a systemic concern that deserves attention. If Rule 1.01 competence increasingly demands technological sophistication, and if sophisticated AI tools remain available only to well-funded lawyers and their clients, a two-tiered justice system may result: wealthy defendants with AI-enhanced representation systematically outperform indigent defendants. This is not hypothetical. It is a present reality, and the defense bar must address it collectively—through training, resource-sharing, and advocacy for adequate public defense funding.

VI. Office Governance: Building an AI-Compliant Practice Every Texas criminal defense practice, whatever its size, should adopt a written AI use policy. At minimum, address the following: Approved Tools Designate which AI platforms are approved for which purposes. Any work touching client information requires enterprise-tier tools with explicit data protection agreements, a no-training-oninputs guarantee, and SOC 2 Type II security certification. Consumer-tier platforms are unfit for client work; limit them to general, non-client-specific research.

Verification Requirements Confirm every AI-generated citation exists in an authenticated database — Westlaw, Lexis, or a verified court website — has not been overruled or negatively treated, and actually supports the proposition cited. The signing attorney must read the holding. This requirement applies to every filing in every court, and you cannot delegate it.

Confidentiality Protocols Tier your protocols to information sensitivity. General legal research unconnected to any client may use less-secure systems. Client facts, defense strategy, privileged communications, and discovery materials should never enter any AI system lacking enterprise-grade security. The most sensitive information—anything that could endanger a witness, privileged strategy, or material

44 NACDL, Parity in Practice at 25–27 (2026) (discussing skill flattening as a risk of over-reliance on AI for junior lawyers, and the

two-tiered justice concern if AI-enhanced representation is available only to clients of well-funded lawyers).

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

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under a restrictive protective order—may warrant exclusion from AI systems entirely, even secure ones.

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Disclosure Requirements Check the local rules and standing orders of every court before filing any AI-assisted document.

Texas’s federal districts impose materially different requirements; complying with one does not ensure compliance with another. When in doubt, disclose.

Client Communication Address AI use in your engagement letters: which tools you may use, what data protections apply, and the client’s opportunity to object. Separately, counsel every client at the outset against using consumer AI platforms to analyze their case, draft communications, or explore their defense strategy. Heppner shows exactly what happens when they do.

Supervision and Training Every staff member who may use AI tools must understand their capabilities and limits, and must know that verification comes before any AI-generated content enters a filing or client communication. The supervising attorney’s obligations travel with the work, no matter who performed it.

Policy Review Schedule Review and update the policy at least annually, and immediately after significant new guidance.

Given how fast this area moves, annual review is the floor; quarterly monitoring of new ethics opinions, court rules, and sanctions decisions is the wiser course.

VII. Practical Guidance for Texas Criminal Defense Practitioners Drawing on Opinion 705, ABA Formal Opinion 512, the NACDL white paper, and the sanctions cases, here is a working framework for current best practices: Vet your platforms before using them with client information. Review terms of service, data retention policies, and privacy protections. The Heppner decision illustrates exactly what can happen when client information enters a consumer AI platform: it may be seized by the government.

Use enterprise-tier accounts with explicit data protection agreements for any client work.

Verify every citation in an authenticated database. Confirm every case an AI tool cites exists in Westlaw or Lexis, has not been overruled or negatively treated, and actually says what you claim.

Read the full passage around the cited proposition. The Stanford hallucination study shows even purpose-built legal tools fail this test more than 17% of the time.

Disclose proactively—to clients and to courts. Address AI use in your engagement letter. Know the AI disclosure rules of every court where you practice—non-compliance is itself a sanctionable omission in districts like the Northern District of Texas.

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Bill honestly. Charge for actual time spent, including time reviewing and verifying AI outputs. Do not charge for time the technology eliminated. Do not pass AI subscription costs to clients as disbursements unless the tool is a standalone client-specific expense the client knows about.

Train your staff. Everyone who touches client files must know which AI tools are approved and what verification they require. Delegating a task does not transfer your ethical obligations to a nonlawyer assistant; they stay with you.

Counsel your clients about their own AI use. Tell them, in writing, not to discuss their case with any AI platform. Make this standard in your representation agreement. Heppner shows exactly what happens if you skip this step.

Engage with AI affirmatively. Use AI to improve the quality and efficiency of your work for clients—in discovery review, legal research, sentencing advocacy, and cross-examination preparation. Competence does not mean avoiding technology; it means using it responsibly. And understanding how AI works is the prerequisite for challenging the AI tools your adversaries deploy against your clients.

Stay current. The AI landscape changes faster than any publication can track. Monitor guidance from the State Bar of Texas, the Texas Center for Legal Ethics, and the ABA. TCDLA built the AI & Anatomy of a Trial seminar precisely to keep practitioners current on a topic that demands ongoing attention, not a single review.

VIII. Conclusion At its core, AI ethics in legal practice comes down to the obligations that have always defined criminal defense: competence, confidentiality, candor, and the unflinching duty to serve clients whose liberty is at stake. Generative AI stress-tests those obligations in new and often unexpected ways, in a landscape changing too fast for any single lawyer to track alone.

Texas criminal defense lawyers face this challenge from a position of relative strength. Opinion 705 provides a clear, practical framework. The TDRPC is up to the task. The sanctions cases— however alarming—provide a clear roadmap of what not to do. The NACDL white paper offers

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Ethics of AI

When you find an error, disclose it immediately. The sanctions cases agree on one point: the cover-up is worse than the error. If AI-generated content in a filing contains a fabricated citation or misrepresentation, notify the court and opposing counsel promptly, withdraw or correct the filing, and explain plainly what happened. The Fletcher and Lnu courts both made clear that candor—especially candor at the first sign of trouble—weighs more than any other factor in setting the severity of sanctions.

Ethics of AI

both the affirmative argument for engaging with AI and a comprehensive governance framework for doing so responsibly.

Hold onto two principles as this area of law keeps evolving. First, verify everything. It is not optional; it is what lawyers do. AI does not change your obligation to make every representation to a court accurate—it only changes where errors can creep in. Second, disclose rather than conceal. When something goes wrong—and in a practice of any volume, something eventually will— transparency costs far less than the alternative.

AI is already in Texas courtrooms. Will criminal defense lawyers engage with it skillfully and ethically, on behalf of their clients? Or will they let it work against them, unchallenged and unexamined, in the hands of better-prepared adversaries? Competence has always demanded the former.

Publishing Note: Yes, generative artificial intelligence was used to assist in producing this article.

However, everything has been independently verified and cited appropriately. Most importantly, the structure, final content, and ideas all come directly from the author.

Brent Mayr is Co-Chair of the TCDLA Ethics Committee and helps staff the TCDLA Ethics Hotline. He is the Managing Shareholder of Mayr Law, P.C. (Houston and The Woodlands), Board Certified in Criminal Law by the Texas Board of Legal Specialization, a former clerk to Judge Barbara Parker Hervery on the Texas Court of Criminal Appeals, and a former Harris County Assistant District Attorney. He is a member of the TCDLA Board of Directors, past President of the Harris County Criminal Lawyers Association, and a member of the faculty at Harvard Law School’s Trial Advocacy Workshop.

Texas Criminal Defense Lawyers Association | AI and the Anatomy of a Trial | September 3–4, 2026

Texas Criminal Defense Lawyers Association

September 3-4, 2026 The Woodlands Resort The Woodlands, Texas

Topic: Defending Against the Machine

Speaker:

Chris Fredericks

2035 Central Cir Ste 209 McKinney, TX 75069-8255 469.712.5741 phone chris@frederickslaw.net email

Defending Against the Machine

AI & Anatomy of a Trial

Defending Against the Machine

Defending Against the Machine: Attacking Law Enforcement's Use of Artificial Intelligence in the Texas Criminal Courtroom A CLE Article Prepared for the TCDLA Seminar AI and the Anatomy of a Trial September 3–4, 2026 — The Woodlands Resort

Table of Contents I. Introduction II. How Law Enforcement Actually Uses AI: A Taxonomy III. Eight Systemic Weaknesses — A Cross-Examination Framework IV. Facial Recognition: The Deep Dive

V. Automated License Plate Readers: The Deep Dive A. The Technology and FLOCK B. The Current, Largely Unfavorable Case Law C. The Framework That Changes the Analysis: Carpenter and Chatrie D. Emerging Litigation to Track Before the Seminar E. Practical Note on FLOCK's GovernmentAction Character

VI. Forensic Analysis and Large-Scale Digital Evidence Review VII. AI-Generated Offense Reports and Probable Cause Affidavits VIII. The Constitutional and Evidentiary Toolkit IX. Practical Trial Strategies IX-A. Model Discovery Requests by Tool Type IX-B. Voir Dire and Jury Instruction Considerations

X. The Regulatory Backdrop: TRAIGA and Federal AI Policy XI. Conclusion Appendix: Articles on AI Mistaken Identity Issues

Defending Against the Machine

A. How the Technology Works B. The Civil-Side Precedent: State of Texas v. Meta Platforms C. The Open Fourth Amendment Questions D. The Kelly/Daubert Battle E. Attacking the Match Even If It Comes In

Defending Against the Machine

I. Introduction Artificial intelligence now touches nearly every stage of a modern criminal case in Texas — the decision to stop a car, the identification of a suspect, the drafting of the offense report, the forensic conclusion offered at trial. Law enforcement agencies have adopted AI tools because they are fast, cheap relative to human labor, and marketed as objective. None of those three qualities is the same thing as reliable, and none of them satisfies the constitutional and evidentiary safeguards that have always governed how the State proves a criminal case.

This article is written for the trial lawyer who will face these tools in the next twelve months, not the policy analyst debating them in the abstract. It proceeds in six parts: (1) a review of how law enforcement actually deploys AI today; (2) eight recurring, provable weaknesses that cut across every category of AI tool; (3) an extended treatment of facial recognition, including the first major Texas-adjacent litigation on point; (4) an extended treatment of automated license plate readers (“ALPRs”); (5) the rapidly developing problem of AI-generated offense reports and probable cause affidavits; and (6) the constitutional and evidentiary toolkit — hearsay, Confrontation Clause, due process, and equal protection — that applies across all of the above.

And a closing section collects practical trial strategies.

A caution before proceeding: law enforcement Artificial Intelligence tools are the wild west.

Reported Texas appellate authority squarely addressing AI evidence is sparse. Much of what follows is necessarily built by analogy — to DNA evidence, to cell-site location information, to eyewitness identification doctrine, and to the forensic-analyst confrontation cases. That analogical work is not a weakness of the defense position; it is exactly how every previous generation of forensic technology was brought under judicial control, and it is the work this article is designed to make usable in a brief or at a hearing.

II. How Law Enforcement Actually Uses AI: A Taxonomy

A. Data Triage and Summarization Departments increasingly use AI to summarize offense reports, body-worn camera footage, jail calls, interview transcripts, emails, and investigative files. The FBI's Criminal Justice Information Services division reportedly uses AI for summarization, research assistance, coding, and writing support, with the stated goal of reducing human review time.

B. Computer Vision and Pattern Recognition These tools scan surveillance footage, detect faces or objects, identify license plates, cluster similar images, and track movement across camera networks. This category includes the two tools given extended treatment below: facial recognition and automated license plate readers.

C. Audio Processing Speech-to-text transcription, speaker separation, and keyword detection on calls, interrogations, and intercepted communications. These raise their own accuracy problems — transcription error, dialect bias, and contextual distortion — that are frequently overlooked because the output looks like a clean transcript rather than a probabilistic guess.

D. Predictive and Analytical Tools Crime-hotspot mapping, lead prioritization, and resource-deployment tools that claim to locate correlations within large, fragmented datasets. These tools are especially exposed to the bias problem discussed in Part III, because they are typically trained on historical arrest and deployment data that reflects historical policing patterns rather than the true underlying distribution of criminal conduct.

E. Generative AI Large language models used for drafting reports, summarizing evidence, and answering internal research questions. Commentators addressing law-enforcement use of these tools have U.S. Dep't of Justice, Artificial Intelligence and Criminal Justice: Final Report (Dec. 3, 2024), https://www.justice.gov/olp/media/1381796/dl.

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The U.S. Department of Justice's December 2024 report, Artificial Intelligence and Criminal Justice: Final Report, issued pursuant to Executive Order 14110, remains the most comprehensive federal survey of AI use across the criminal justice system. The report covers identification and surveillance, forensic analysis, predictive policing, and risk assessment.1 For the trial lawyer, the DOJ's categories collapse usefully into five buckets.

Defending Against the Machine

emphasized that while they can improve speed, they require strong internal safeguards because open, consumer-grade systems can mishandle confidential information or generate fluent but false output — a point developed at length in Part V.

III. Eight Systemic Weaknesses — A Cross-Examination Framework The following eight categories are not specific to any single tool. They recur across facial recognition, ALPR, forensic analysis, and generative report-writing alike, and they should form the backbone of any Kelly/Daubert challenge, suppression motion, or cross-examination outline involving AI evidence.

Generative tools can produce fluent but false summaries, invented citations, nonexistent facts, or unsupported inferences. Even when a tool is used only for drafting or summarization, false content can migrate into offense reports, warrant affidavits, charging memoranda, or trial preparation materials — and downstream readers often trust the polished output more than the underlying record it was supposedly built from.

2. Bias in Training Data and Outputs AI systems can reproduce or amplify historical bias present in training data, arrest records, deployment patterns, or labeled exemplars. This risk is most acute in facial recognition, predictive policing, and threat-scoring tools built on historically skewed law-enforcement datasets. Bias matters legally because it can affect who becomes a suspect, who is surveilled, which neighborhoods receive disproportionate attention, and how ambiguous evidence gets interpreted.

3. Explainability Problems Many AI systems are effectively black boxes from the testifying officer's perspective. The officer typically knows the output but not how the tool reached it — not the training data, not the thresholds applied, not the validation methodology. That opacity complicates judicial gatekeeping, defense discovery, confrontation, and effective cross-examination at every subsequent stage.

4. Error Rates and Validation Gaps AI tools vary enormously in quality. Some are tested on narrow, well-defined tasks; others are vendor-driven products with little independent validation. Error rates can differ by race, sex, age, accent, recording quality, lighting, camera angle, and compression artifacts, meaning a tool validated in controlled laboratory conditions can perform far worse in the field. Without robust, published validation studies, courts and defendants cannot meaningfully assess reliability under evidentiary frameworks built around testing, peer review, and known error rates.

5. Contamination of Human Judgment (Automation Bias) AI need not be admitted at trial to matter. It can shape officers, analysts, and prosecutors long before the courtroom phase begins. The well-documented phenomenon of automation bias, in

Defending Against the Machine

1. Hallucinations and Fabricated Outputs

which humans defer to machine output even when they should not, is a significant problem in AI reliance. An AI-generated suspect suggestion, timeline summary, or threat assessment can bias subsequent witness interviews, lineup construction, charging decisions, and the interpretation of ambiguous facts.

Defending Against the Machine

6. Reliability of Synthetic and Digital Media As AI-generated audio, video, and images proliferate, courts face the problem from both directions: AI can help sort and analyze digital evidence, but AI also makes evidence dramatically easier to fabricate, alter, or manipulate. The prosecution may rely on AI to organize a large media set while the defense simultaneously argues the underlying media is synthetic, incompletely preserved, or insufficiently authenticated.

7. Privacy and Confidentiality Risks Open or poorly governed AI systems risk exposing confidential investigative information, personal data, witness identities, or sealed material. In policing specifically, such risks implicate statutory privacy regimes, due process concerns, and internal policy violations. Commentary from law-enforcement policy circles has repeatedly stressed the need for agency-approved tools, IT review, and legal vetting before official deployment.

8. Overclaiming Precision AI outputs frequently look precise — match scores, confidence intervals, ranked candidate lists, tightly written summaries — and that apparent precision can mislead judges and jurors into overestimating actual accuracy. Courts have worried for decades about overstated scientific certainty in forensic evidence generally. AI risks reproducing that problem in a new and more persuasive-looking form. The line for closing argument nearly writes itself: a polished result is not the same thing as a true one.

IV. Facial Recognition: The Deep Dive A. How the Technology Works

B. The Civil-Side Precedent: State of Texas v. Meta Platforms Case law directly addressing AI facial recognition and personal rights remains extremely limited given the technology's recent vintage, but the most significant Texas-adjacent authority is civil rather than criminal. In State of Texas v. Meta Platforms, Inc., Cause No. 22-0121 (71st Judicial District Court, Harrison County, Texas), the Texas Attorney General alleged that Meta used facial-recognition technology to extract facial geometry from users' photographs without consent, in violation of the Texas Capture or Use of Biometric Identifier Act (“CUBI”) and the Deceptive Trade Practices Act.2 Meta ultimately settled for $1.4 billion — the largest settlement ever obtained by a single state — without admitting wrongdoing. The case was filed in 2022 and resolved in July 2024, with the agreed final judgment entered in the Harrison County district court.

Although Meta was not a criminal case, its treatment of facial geometry as sensitive biometric data captured without consent is a useful analogy for arguing that individuals retain a Fourth Amendment-adjacent privacy interest in their own facial identifiers — particularly under the Texas Constitution's independent privacy protections. Further, the Attorney General’s role in litigating this issue complicates the prosecutor’s ability to argue conversely on facial recognition privacy concerns.

C. The Open Fourth Amendment Questions No published Texas appellate criminal decision squarely addresses the admissibility of facialrecognition testimony. The core unresolved questions are: (1) whether a facial-recognition match alone establishes probable cause; (2) whether running the search constitutes a “search” under Tex. Att'y Gen., Attorney General Ken Paxton Secures $1.4 Billion Settlement with Meta (2024), https://www.texasattorneygeneral.gov/news/releases/attorney-general-ken-paxton-secures-14-billion-settlementmeta-over-its-unauthorized-capture; McKool Smith, Texas Obtains $1.4B Settlement From Meta over Facial Recognition Data (July 30, 2024).

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Facial recognition tools compare an unknown suspect image against databases of known identities — typically state driver's license photographs, jail photographs, and prison photographs — and return a ranked list of potential matches, each associated with a probability score. The software generates investigative leads; it does not, by itself, identify anyone with certainty. There are three distinct avenues to challenge law enforcement's use of these tools: (1) the invasive, nonconsensual capture and use of the underlying identity databases; (2) the reliability and admissibility of the tool's output at a hearing or trial; and (3) the factual reliability of the specific match as applied to a particular client's case.

Defending Against the Machine

Article I, Section 9 of the Texas Constitution; (3) whether facial-recognition evidence satisfies the Kelly/Daubert reliability standards; and (4) whether a misidentification generated by the software can itself violate due process.

Texas courts will likely resolve these questions by analogy to tangentially related lines of authority: traditional eyewitness-identification doctrine, digital-forensics admissibility case law, and the expectation-of-privacy framework developed for location-tracking technology. That framework runs through United States v. Jones, 565 U.S. 400 (2012) (warrantless GPS tracking is a search); Carpenter v. United States, 138 S. Ct. 2206 (2018) (cell-site location information is a search, notwithstanding the third-party doctrine); and Holder v. State, 595 S.W.3d 691 (Tex. Crim.

App. 2020) (adopting Carpenter's reasoning under Article I, Section 9 to hold that twenty-three days of cell-site data required a warrant).

Facial recognition is not confined to trial; the same software frequently underlies arrest and search warrants, making the threshold question one of the technology's sufficient reliability to establish probable cause in the first instance. Three suppression arguments follow: first, a match is insufficiently reliable, standing alone, to establish probable cause without independent corroboration. Ideally a properly conducted blind photo lineup with a fact witness, since presenting the officer with the AI “match” before an identification procedure creates exactly the automationbias contamination described previously. Second, the software's training data, error rates, and bias metrics are typically undisclosed, undermining the factual basis for the warrant. Third, if the underlying biometric data was captured without statutory authority — as alleged in Meta — the resulting identification may be fruit of the poisonous tree.

D. The Kelly/Daubert Battle Texas analyzes scientific testimony under Kelly v. State, 824 S.W.2d 568 (Tex. Crim. App. 1992), applying reliability principles drawn from Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993). At a Kelly hearing on facial-recognition evidence, counsel should press: (1) the tool's known false-positive and false-negative rates; (2) whether the algorithm has been peer reviewed; (3) whether it is generally accepted in the relevant scientific community; (4) whether the training data and model architecture are transparent; (5) whether the system has been independently validated; (6) whether the specific match was operator-assisted or fully automated; and (7) whether the system falls within any statutory high-risk classification under Texas's new AI statute.

The strategic fight over Kelly has two sides. The defense position is that facial-recognition systems are proprietary, opaque, and subject to demonstrated racial and demographic disparities in error rates, and therefore fail Kelly's reliability prongs absent detailed validation evidence from the State. The State will typically try to characterize facial recognition as a computer-assisted

comparison tool akin to enhancement software or ALPRs — not “scientific evidence” at all, and therefore outside Kelly's reach. The better analogy, and the one defense counsel should press, is to DNA comparison or gas chromatography testimony: like those disciplines, facial recognition generates a probabilistic match through an opaque statistical process that a lay witness cannot meaningfully explain without expert support.

E. Attacking the Match Even If It Comes In Facial-recognition software is only as reliable as the image quality and the comparison database. A grainy or partial image will never be as reliable as a high-definition frontal photograph, but the more consequential problem is what happens to the rest of the ranked list. A “match” is a probability, and matches in the 70–85% range — or several closely clustered candidates — are common. Initial discovery often contains only the final report identifying the top candidate, omitting two to five other candidates the software flagged at only slightly lower percentages. If a detective never investigated those alternate candidates — or the probable cause affidavit omitted them — that is both a factual defense on identity and a potential Franks issue. Demand the complete, unredacted ranked output, not merely the summary report the agency chose to disclose.

3

U.S. Dep't of Justice, Artificial Intelligence and Criminal Justice: Final Report (Dec. 3, 2024).

Defending Against the Machine

Federal guidance supports the defense position. The Department of Justice's National Institute of Justice has repeatedly warned that AI systems used in criminal justice can produce false identifications, unreliable pattern recognition, and algorithmic errors, and that such systems develop independent, complex parameters that can be wrong, uninterpretable, or misleading.3

V. Automated License Plate Readers: The Deep Dive

Defending Against the Machine

A. The Technology and FLOCK ALPRs use AI to identify stolen vehicles, track movement, and flag wanted subjects. FLOCK Safety is the private vendor most commonly used by Texas law enforcement, allowing agencies to query known plates for historical locations or to search a given area and time window for vehicles present during a reported crime. Larger cities are seeing a proliferation of government sponsored cameras installed on city streets. Harris County renewed its FLOCK contract for access to 480 ALPR cameras across Houston-area streets — a locally relevant fact for venue-specific motions and voir dire.4

B. The Current, Largely Unfavorable Case Law Reported ALPR decisions to date have mostly favored the government, typically applying the plain-view doctrine to the exterior of a vehicle: ●

United States v. Sturdivant, No. 1:22-cr-615 (N.D. Ohio June 9, 2025) — the court denied a motion to suppress, holding that a defendant has no reasonable expectation of privacy in the exterior appearance of his car or a temporary tag, and that ALPR database queries present, at most, only a marginal extension of prior plain-view precedent. The court expressly limited its holding to the facts presented and declined to address every possible form ALPR surveillance could take.

●

United States v. Yang, 958 F.3d 851 (9th Cir. 2020) — the Ninth Circuit expressly declined to reach the Fourth Amendment question. Correction to the source paper: the panel's holding rested on standing, not on any expired vehicle registration. Yang had retained a rental vehicle past the return date, and the rental company had already attempted repossession by remotely disabling the vehicle's GPS unit; the court held that under those facts Yang had no reasonable expectation of privacy in the vehicle's historical location data. Counsel should not cite Yang for an “expired registration” rationale — the correct hook is the lapsed/repossession-triggered rental agreement.

●

Commonwealth v. McCarthy, 142 N.E.3d 1090 (Mass. 2020) — correction to the source paper: this was decided by the Massachusetts Supreme Judicial Court (SJC), not the “Massachusetts Supreme Court.” The SJC affirmed the denial of a suppression motion involving four fixed ALPR cameras on two bridges, holding that this limited use did not constitute a search, but explicitly signaled that more “widespread use” of ALPR networks

See ABC13 Houston, Skeptical residents question Harris County's renewed Flock camera contract, https://abc13.com/post/skeptical-flock-cameras-harris-county-renews-contract/19190111/ (verify current figures directly with the vendor contract before citing in a filing, as camera counts are frequently updated).

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could implicate constitutional protections — language later courts and defense counsel have leaned on heavily.

C. The Framework That Changes the Analysis: Carpenter and Chatrie

That framework was extended in Chatrie v. United States, 609 U.S. ___, No. 25-112 (decided June 29, 2026). Chatrie arose from a geofence warrant compelling Google to disclose Location History data for devices within 150 meters of a bank robbery over a series of expanding time windows. The Supreme Court held that individuals retain a reasonable expectation of privacy in their digital location data even where a third party (Google) holds it in the cloud, and that police therefore conducted a Fourth Amendment search when they obtained Chatrie's location history — the first constitutional ruling on geofence warrants in the country.5 The Court did not resolve whether the specific three-step geofence process used in that case satisfied the Fourth Amendment's probable cause and particularity requirements, remanding that question for further proceedings.6 Read together, Carpenter and Chatrie do not automatically make ALPR searches unconstitutional — the law here remains transitional, with CSLI and geofence data now governed

by Supreme Court precedent while ALPR is still being worked out in the lower courts, legislatures, and municipal policy debates. But the two cases supply the strongest available template for the ALPR argument: (1) establish a reasonable expectation of privacy in continuous monitoring of travel on public roads by shifting the frame away from “public roads” and toward the facts that the government installed the cameras, that the resulting data is retained and searchable for months or years, and that camera placement is pervasive across the relevant jurisdiction; and (2) draw the analogy to the GPS mosaic-surveillance theory from United States v. Jones, 565 U.S. 400 (2012).

D. Emerging Litigation to Track Before the Seminar Chatrie v. United States, 609 U.S. ___ (2026) (slip op.); see also NACDL, Chatrie v. United States case summary, https://www.nacdl.org (last visited July 2026).

6 Id. (remanding for consideration of the warrant's particularity and reasonableness).

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The strongest existing framework for arguing that long-term, aggregated ALPR surveillance crosses into a Fourth Amendment search is the cell-site/location-data line of cases, not the plainview ALPR cases above. In Carpenter v. United States, 138 S. Ct. 2206 (2018), the government obtained 12,898 location points over 127 days — an average of 101 location points per day. The Supreme Court held that individuals retain a reasonable expectation of privacy in this aggregated cell-site location information notwithstanding the third-party doctrine, reasoning that the Fourth Amendment exists both to protect the “privacy of life” from arbitrary power and to place obstacles in the way of permeating police surveillance.

Defending Against the Machine

Two active cases illustrate that this fight is accelerating, not settled. In Schemel v. City of Marco Island, the New Civil Liberties Alliance is asking the Eleventh Circuit to revive a suit challenging Marco Island, Florida's continuous ALPR monitoring of residents without suspicion or warrants, arguing the district court read Carpenter too narrowly by limiting it to CSLI-equivalent data.7 And in November 2025, the Electronic Frontier Foundation and ACLU of Northern California sued the City of San Jose and its police department over an alleged 261,711 warrantless ALPR database queries between June 2024 and June 2025, arguing the department's practices amount to invasive location tracking without any individualized suspicion requirement.8 Both cases are worth monitoring for decisions before the September seminar date, as either could sharpen the ALPRas-search argument considerably.

E. Practical Note on FLOCK's Government-Action Character Courts analyzing ALPR cases have frequently glossed over the fact that many of the cameras in dispute are municipally installed — on light posts, traffic signals, or patrol vehicles — rather than genuinely private business records voluntarily conveyed to a third party, as in the classic third-party-doctrine cases. That distinction matters: FLOCK-fed ALPR data is governmentordered and government-financed surveillance from the outset, not a business record the State later subpoenas. Counsel should press this distinction hard, since it removes the third-party doctrine's core justification — voluntary disclosure to a private entity — from the analysis entirely.

Because ALPR data is typically used to generate investigative leads rather than to record the charged offense itself, the practical litigation posture in nearly every case will be a motion to suppress directed at an arrest or search warrant, not a trial objection to the ALPR data's admission.

New Civil Liberties Alliance, NCLA Asks Eleventh Circuit to Revive Suit Against Marco Island, FL's Warrantless Tracking of Drivers (Dec. 16, 2025).

8 The Register, San Jose's 'warrantless' license plate queries land cops in court (Nov. 19, 2025).

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VI. Forensic Analysis and Large-Scale Digital Evidence Review A. AI-Assisted Forensic Tools

The recurring question across every one of these applications is the same: what underlying method or scientific basis is the AI actually using to reach its stated conclusion, and is there analyst bias baked into how the AI-suggested result gets reported? A Kelly/Daubert hearing remains the most effective vehicle for testing that question, for the same reasons developed in Part IV above.

B. Analysis of Large Digital Files AI tools increasingly assist forensic examiners in searching large, unorganized collections of photographs and video for specific content — weapons, particular individuals, or flagged categories of imagery — and natural-language-processing tools help identify relevant files and communications by topic. Machine translation assists examiners working across multiple languages, and AI is increasingly used to detect whether evidence itself was created or altered by AI (deepfake detection). Most of this use supports investigative steps rather than expert conclusions offered directly at trial, which means the defense's primary discovery obligation is to confirm that human analysts actually followed up on AI-flagged leads rather than treating the flag itself as a conclusion.

C. Authentication and the Underlying-Data Problem Where AI processes or interprets digital media, authentication is the first line of attack.

Counsel should compare any AI-generated transcript or summary against the original recording for consistency, and should demand the underlying original file or dataset — not merely the final report a vendor's software produced from it. This issue arises constantly with cell-phone extractions: defense counsel typically receives a Cellebrite-generated report the investigating officer ran and interpreted, not the raw extraction file itself. The underlying data is the complete forensic record; the vendor report is a curated subset tailored for presentation. A full defense evaluation requires comparing the report against the underlying extraction to confirm no material evidence was omitted.

If AI altered, enhanced, summarized, or compressed the evidence in question, the defense should argue that any enhancement changes the character of the exhibit and, at minimum, requires

Defending Against the Machine

AI increasingly assists crime-scene technicians with fingerprint analysis, DNA swabbing, and scene documentation, including real-time guidance delivered through wearable devices. The same tools evaluate ballistics, tool marks, and trace evidence, and increasingly perform complex statistical DNA-mixture interpretation on samples too small or degraded for traditional methods, producing probability-based conclusions about potential contributors.

Defending Against the Machine

a more substantial evidentiary foundation before admission — circling back to the Kelly/Daubert framework. This also raises a Confrontation Clause dimension addressed in Part VII below: is the sponsoring witness capable of explaining the underlying science and theory behind the software that generated the enhanced exhibit or summary conclusion, or is the witness merely relaying the machine's output?

VII. AI-Generated Offense Reports and Probable Cause Affidavits A. The Scope of the Problem

The institutional adoption pattern was widely observed in both California and Missouri.

Ferguson, Missouri — a St. Louis suburb whose police department became a national flashpoint after the 2014 shooting of Michael Brown — signed a five-year, $537,000 contract for Axon Body 4 cameras bundled with Draft One software, and the “assist-only” model is already working its way into regional police-academy training even in neighboring departments that publicly disclaim using it.10 The real-world track record is decidedly mixed, and departments are already discovering this the hard way. The Anchorage Police Department concluded a three-month trial of Draft One and declined to retain the technology after finding it did not, in fact, save officer time.11 A June 2025 report from Fair and Just Prosecution catalogued accuracy and disclosure concerns across multiple agencies piloting the technology, and a December 2025 EFF year-in-review found that Draft One is now used for a wide array of incidents — including serious felonies — with most departments imposing no restriction on its use by crime type.12 i. The Linguistic and Dialect Bias Problem A distinct and independently documented weakness compounds the hallucination risk described in Part III: automatic speech recognition models built into tools like Draft One perform measurably worse on African American Vernacular English (AAVE) and non-native English John Schleiffarth, The Ghost in the Machine: Defending Against the Rise of AI-Generated Police Reports, The Champion, July 2026, at 16, 17 (citing Axon, Draft One vs. Other Generative AI Solutions for Police Report Writing (last visited Mar. 2026)); see also EFF, Anchorage Police Department: AI-Generated Police Reports Don't Save Time (Mar. 2025); ACLU, AI-Generated Police Reports Raise Concerns Around Transparency, Bias (Dec. 2024).

10 Schleiffarth, supra, at 16 (citing Ferguson City Council Res. 2024-52 (adopted 2025); St. Louis Metro. Police Dep't, 2025 Surveillance Use Plan (Dec. 2024)).

11 EFF, Anchorage Police Department: AI-Generated Police Reports Don't Save Time (Mar. 2025).

12 Fair and Just Prosecution, AI-Generated Police Reports: High Tech, Low Accuracy, Big Risks (June 2025); EFF, AI Police Reports: Year in Review (Dec. 23, 2025).

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Law enforcement agencies are increasingly using generative AI to write or improve offense reports. Axon's Draft One is the most widely deployed example: the tool feeds body-worn camera audio into a version of OpenAI's GPT-4 Turbo, which produces a structured narrative report in seconds rather than the thirty to forty-five minutes such reports typically take an officer to draft by hand, with the officer expected to review, edit, and sign the result before it is filed under oath.9 Draft One deliberately limits its analysis to audio rather than video, because its developers are aware that visual processing produces materially worse hallucinations — a design choice that itself creates a defense opening addressed in subsection D below.

Defending Against the Machine

speech patterns. Separate peer-reviewed research has found that large language models encode what researchers describe as covert dialect prejudice — systematically less favorable inferences about a speaker's character, employability, and criminality based on dialect features alone.13 This is not a hypothetical concern for a jurisdiction with any meaningful population of non-native English speakers or AAVE speakers — it is a documented, published finding directly bearing on transcription accuracy in exactly the recordings these tools process. ii. Translation Failures: The Cruz-Zamora Cautionary Tale A related and cautionary illustration involves not offense-report drafting but real-time translation during a stop. In United States v. Cruz-Zamora, a Kansas Highway Patrol trooper used Google Translate to ask a Spanish-speaking motorist for consent to search his vehicle. The English question “Can I search the car?” rendered into Spanish as “¿Puedo buscar el auto?” — which, translated back into English, means “Can I find the car?” rather than “Can I search the car?” A professional interpreter later testified that the Spanish phrase was a literally correct word-for-word rendering, but not the question the trooper actually intended to ask. The subsequent search uncovered roughly fourteen pounds of methamphetamine and cocaine, which the court later suppressed because the consent was not unequivocal, specific, and freely and intelligently given.14 The case predates the current generation of AI report-writing tools, but it illustrates precisely the same underlying failure mode: a machine-translation or transcription layer can produce output that is technically defensible and substantively wrong, and the officer relying on it may have no way to recognize the gap in real time. iii. Automation Bias and the 'Rubber Stamp' Problem Every department policy examined in the most comprehensive current survey of this issue requires the reviewing officer to “review and edit” the AI draft before signing it — a sensiblesounding safeguard undermined by the well-documented psychological phenomenon of automation bias, the tendency to over-rely on automated output. An exhausted officer at the end of a shift is not scrutinizing every sentence; he is reading for obvious errors, accepting fluent prose, and signing his name to a document he did not write and may not fully remember. The Electronic Frontier Foundation's investigation of Draft One specifically raised this concern, questioning

13 Allison Koenecke et al., Racial Disparities in Automated Speech Recognition, 117 Proc. Nat'l Acad. Sci. 7684 (2020); Valentin Hofmann et al., Dialect Prejudice Predicts AI Decisions About People's Character, Employability, and Criminality, 633 Nature 147 (2024); Andrew Guthrie Ferguson, Generative Suspicion and the Risks of AIAssisted Police Reports, 120 Nw. U. L. Rev. 299 (2025).

14 United States v. Cruz-Zamora, No. 17-40100, 2018 U.S. Dist. LEXIS 93906 (D. Kan. June 4, 2018); see Schleiffarth, supra, at 17.

whether officers are, in practice, reflexively rubber-stamping AI drafts to move on as quickly as possible.15 iv. The Audio-Only Blind Spot

v. The 'Draft Zero' Discovery Motion Prosecutors will typically argue that an AI tool's first-pass output is preliminary work product, and that only the final, officer-signed report matters for discovery purposes. Counsel should not accept that framing. Where a jurisdiction's discovery rule reaches broadly — Missouri's rule, for example, entitles the defense to any arrest or incident reports and any electronic data relating to the offense — the original, unedited AI draft is discoverable in its own right, and any material the officer deleted between the first AI-generated draft and the final signed version is independently significant: if the AI's first pass included a detail the officer later removed, that deletion is potentially exculpatory information the State must disclose.17 Two states have now moved to make this an explicit statutory right rather than a matter of discovery-rule interpretation. California's SB 524, effective January 1, 2026, requires law enforcement agencies to retain the first AI-generated draft of a report for as long as the final report itself is retained, and requires disclosure on the face of the report that it was written, in whole or in part, using AI — so that judges, defense counsel, and auditors can identify which portions of the final report were written by the officer and which were generated by the computer. Utah's SB 180, effective May 7, 2025, imposes a similar disclosure-and-certification requirement.18 Texas has no comparable statute as of this writing. Until it does, Texas defense counsel are left to build the same protection through discovery motions and Brady practice rather than a clean statutory Matthew Guariglia & Dave Maass, Axon's Draft One Is Designed to Defy Transparency, Elec. Frontier Found.

Deeplinks (July 10, 2025); see also Ctr. for Democracy & Tech., Automated Police Report Drafting (Mar. 2026) (“Officers may rely on AI drafts rather than their own careful recollection and judgment ... lead[ing] to superficial acceptance of text that does not accurately reflect the encounter.”).

16 Axon, A Closer Look at Draft One (2024) (“Draft One analyzes the transcript from the camera. ... If any details do not exist in the transcript, they are left out of the report.”).

17 Mo. Sup. Ct. R. 25.03; John Schleiffarth, The Ghost in the Machine, The Champion, July 2026, at 17.

18 Cal. S.B. 524, § 13663 (2025); Utah S.B. 180, 2025 Gen. Sess. (effective May 7, 2025); see Andrew Guthrie Ferguson, Concerns About AI-Written Police Reports Spur States to Regulate the Emerging Practice, The Conversation (Oct. 15, 2025).

15

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Because Draft One analyzes only audio, any visually descriptive detail appearing in a resulting report — a “furtive reaching motion,” a “suspicious gesture” — could not have come from the AI tool itself. Either the officer added the detail, or the model hallucinated something it could not possibly have heard. Either way, that gives the defense a clean opening: demand to know the specific source of any visual description in an AI-assisted report, since the tool's own vendor documentation confirms it works from audio transcription alone and leaves out anything the transcript does not contain.16

hook — which is precisely the kind of gap a TCDLA legislative committee may want to flag for the next session.

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vi. Impeaching 'Independent Recollection' When an officer relies on an AI-assisted report to testify, his claim to independent recollection is both legally and practically compromised: he refreshed his memory with a document he did not write. The cross-examination line writes itself — walk the officer through the fact that the report was generated, at least in part, by software; that he did not personally compose every sentence; that he reviewed rather than authored the narrative before signing it; and that, as a result, his testimony today rests at least in part on his memory of a document he did not fully write. An officer who cannot identify which sentences came from the AI and which came from him, and who cannot say whether the AI introduced any errors, has a serious and visible credibility problem on the very report the State is asking the jury to trust.

B. Attacking the Warrant Affidavit The most consequential version of this problem arises when AI assists in drafting a searchwarrant affidavit. Defense counsel should scrutinize whether the affiant personally verified every factual assertion in the affidavit, whether AI introduced inaccuracies, and whether material omissions or distortions affected the probable cause determination. It is also fair game to ask whether the magistrate was ever informed that AI assisted in preparing the document. Where the affidavit contains false statements or reckless omissions introduced through AI-assisted drafting, counsel should raise a suppression challenge analogous to Franks v. Delaware, 438 U.S. 154 (1978).

VIII. The Constitutional and Evidentiary Toolkit A. Hearsay and Machine-Generated Statements

Even where classical hearsay doctrine does not squarely apply, the same argument works as a reliability objection at a suppression or admissibility hearing. Get the hearing, and make the sponsoring witness explain the tool's operation in plain terms. The more thoroughly the process gets explained on the record, the clearer it becomes that this is not a calculator performing simple arithmetic, but a logic-based program carrying internal biases traceable to its design choices.

B. Confrontation Clause AI-generated outputs that function as testimonial assertions — paired with a witness who merely repeats a machine-generated conclusion without understanding the underlying process — raise serious Confrontation Clause concerns. The strongest analogy is Burch v. State, 401 S.W.3d 634 (Tex. Crim. App. 2013), in which the Texas Court of Criminal Appeals held that a lab report identifying a substance as cocaine could not come in through a reviewing analyst who lacked personal knowledge of whether the underlying tests were performed correctly — the reviewer could not verify the results were properly generated, even though the actual testing was performed by an instrument rather than a person.19 Argue the AI-summary problem should be treated no differently: a surrogate witness who can testify only that the software produced a given output — without being able to explain the method, assumptions, or limitations behind it — offers exactly the kind of uncomfortable, accusationsupporting testimony Burch forecloses. Confrontation Clause litigation specific to AI-generated evidence remains underdeveloped, and many courts will initially resist treating a machine output as a “witness” in any formal sense. The point to press is that constitutional confrontation loses its Burch v. State, 401 S.W.3d 634, 637 (Tex. Crim. App. 2013) (rejecting the argument that no Confrontation Clause violation occurs where an analyst is 'only interpreting machine-generated data,' because 'the testimonial statements were therefore those of the machine').

19

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Some AI outputs raise hearsay concerns depending on how the State presents them.

Prosecutors will often characterize an AI-generated summary as a mere tool output rather than a statement, but where the government offers the summary substantively — to prove what actually happened — defense counsel can argue the summary is an out-of-court assertion generated by a system that cannot be cross-examined. The better framing focuses on the fact that the summary reflects pre-programmed human choices: the designers' training data, prompt design, classification rules, and threshold settings. The output may appear to “self-generate,” but that self-generation was itself the product of a pre-ordained software and logic design — making the result less a neutral machine readout and more a human-mediated assertion in disguise.

substance if the State can introduce opaque, accusation-supporting outputs through a surrogate who cannot answer meaningful questions about reliability.

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C. Due Process and Fundamental Fairness AI-related problems can also be framed as due process violations. Where the State relies on undisclosed, untestable, or demonstrably biased systems in a way that materially affects liberty, counsel can argue the proceedings have ceased to be fundamentally fair — particularly where the government withholds source code, validation data, or configuration settings essential to adversarial testing. A closely related theory sounds in Brady/Giglio: where the government possesses information about a tool's limitations, internal doubts, prior failures, or inconsistent field performance, and fails to disclose known error rates or exculpatory limitations, that omission implicates the State's disclosure obligations under Brady v. Maryland, 373 U.S. 83 (1963), and Giglio v. United States, 405 U.S. 150 (1972).

D. Equal Protection and Selective Impact Where an AI system disproportionately misidentifies or burdens members of a protected group, counsel may explore equal protection arguments or, more practically, use disparate-impact evidence to undermine the tool's overall reliability and fairness. Pure equal protection claims are difficult absent proof of discriminatory intent, but evidence of skewed performance — for instance, ALPR cameras disproportionately concentrated in a particular racial or economic demographic — can be powerful in suppression motions, admissibility hearings, and jury argument even where a formal equal protection claim would not survive on its own.

IX. Practical Trial Strategies Even where a motion to exclude AI evidence fails outright, defense counsel retains substantial tools to reduce its persuasive force at trial.

Cross-examine for comprehension, not just conclusion. The central question is whether the testifying officer actually understands how the system works — not whether the system produced an impressive-looking result. Establish, plainly, that the vendor (not the witness) possesses knowledge of the model's assumptions, and that the tool was never independently validated on the specific facts of this case.

●

Reframe the output as a suggestion, not a conclusion. AI software produces a suggestion that human investigators then relied upon — often to their detriment, developing tunnel vision as a result. Jurors respond well to the idea that they are being shown a polished result stripped of the underlying uncertainty that accompanies any analytical tool.

●

Hire an expert where the case can bear the cost. A qualified defense expert can explain why confidence scores and match percentages are routinely misunderstood by lay jurors, how false positives and false negatives occur, why real-world model performance depends heavily on field conditions rather than laboratory conditions, and how training data can encode bias invisibly. Expert testimony also develops the summarization-omission point far more persuasively than defense cross-examination alone.

●

Demand the raw data at every turn. Whether it is the complete ranked facial-recognition candidate list, the underlying cell-phone extraction file, or the original body-camera audio behind an AI-generated report, the vendor-curated summary report is never a substitute for the underlying record, and the distinction is worth making explicitly to the court every time the State offers a curated version.

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●

IX-A. Model Discovery Requests by Tool Type

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Because nearly every argument in this article ultimately depends on getting past the vendor's curated summary to the underlying data, the discovery request is often the single most consequential document defense counsel files. The requests below are organized by tool type and are meant to be adapted to the specific vendor and jurisdiction involved.

Facial Recognition ●

The complete, unredacted ranked list of candidate matches returned by the software, including all candidates below the threshold the agency chose to disclose, with associated confidence/probability scores for each.

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The name and version number of the software and algorithm used, and any change log or update history applicable to the relevant time period.

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Any independent validation studies, NIST Facial Recognition Vendor Test results, or internal agency testing data concerning the specific algorithm version used.

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The source and composition of the comparison database (driver's license, jail, prison, or other), including its size and any known demographic composition data.

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Whether the match was reviewed by a human examiner prior to use in the investigation, and if so, that examiner's qualifications and the protocol followed.

Automated License Plate Readers ●

The complete query log showing every search run against the ALPR database in connection with the investigation, not merely the query that produced the report offered in discovery.

●

The data-retention policy applicable to the database at the time of the search, and the actual retention period for the specific data queried.

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The number, location, and ownership (municipal vs. vendor-owned) of every camera contributing data to the queried database within the relevant geographic area.

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Any inter-agency data-sharing agreements governing access to the database by outside agencies.

AI-Generated Reports and Affidavits ●

Whether the report or affidavit at issue was drafted, in whole or part, using an AI tool, and the identity and version of that tool.

●

The original, unedited first-pass AI-generated draft (the “Draft Zero”), together with a redline or comparison showing all human edits made before the report was finalized — request this as discoverable case material under the jurisdiction's general document/electronic-data discovery rule.

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The underlying audio or video recording the AI tool analyzed to generate the draft.

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Any agency policy governing officer review and verification of AI-generated drafts before submission, and documentation that the specific report at issue complied with that policy.

Digital Forensic Extractions The complete raw extraction file (e.g., the full Cellebrite or GrayKey image), not merely the vendor-generated report summarizing it.

●

The extraction software's version number and any known limitations or error reports applicable to that version.

●

Documentation of the chain of custody for the device and the extraction file from seizure through analysis.

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●

IX-B. Voir Dire and Jury Instruction Considerations

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Jurors arrive with a strong cultural presumption that computers are more objective and less error-prone than human judgment. That presumption is the single biggest obstacle to a fair hearing of any AI-evidence defense, and it should be addressed directly rather than left to surface unexamined during deliberations.

Voir Dire Themes ●

Explore jurors' personal experience with AI tools that got something wrong — autocorrect errors, GPS misdirection, voice-assistant misunderstandings — to normalize the idea that AI tools produce confident-looking but incorrect output as a matter of routine.

●

Probe for jurors who may over-credit a percentage-based match score without understanding what the percentage actually represents or how it was calculated.

●

Identify jurors who work in fields involving statistical or probabilistic reasoning (data analysis, actuarial work, clinical lab work) who may be particularly useful — or particularly resistant — to the defense framing, depending on their own professional experience with model reliability.

Jury Instruction and Closing Themes ●

Where available, request an instruction analogous to those given for eyewitness identification or accomplice-witness testimony, cautioning the jury that a computer-generated match or summary is not, by itself, conclusive proof of the fact it purports to establish.

●

In closing, return to the plain-language framing developed throughout this article: a polished result is not the same thing as a true one, and the State's own witness could not explain how the tool reached its conclusion.

X. The Regulatory Backdrop: TRAIGA and Federal AI Policy

At the state level, the Texas Responsible Artificial Intelligence Governance Act (TRAIGA), House Bill 149, was signed into law on June 22, 2025, and became effective January 1, 2026, making Texas one of the first states, alongside Colorado and Utah, to enact comprehensive AI governance legislation. TRAIGA imposes duties on developers, distributors, and deployers of “high-risk” AI systems; requires certain disclosures when a consumer interacts with an AI system, including a specific disclosure duty for healthcare providers; and is enforceable exclusively by the Texas Attorney General, with civil penalties of up to $200,000 per violation and a sixty-day cure period, but no private cause of action.20 Two practical points follow for defense counsel. First, TRAIGA's enacted scope is narrower than the original December 2024 draft, which had proposed a EU AI Act/Colorado-style framework built around categorical “high-risk” designations tied to “consequential decisions.”

Much of that framework was scaled back before passage, meaning any argument that a particular law-enforcement AI tool is a statutorily defined “high-risk system” needs to be checked against the final, enacted statutory text — not the original bill or secondary summaries (including this one). Second, and more usefully for present purposes, TRAIGA does not preempt or displace any of the constitutional and evidentiary arguments developed in Parts IV through VIII of this article; it is a regulatory compliance statute enforced by the Attorney General, not a substitute for a Fourth Amendment suppression motion, a Kelly/Daubert challenge, or a Confrontation Clause objection.

Its principal value to the defense bar is likely to be as a source of discoverable compliance documentation — risk-identification policies, impact assessments, and required disclosures — that vendors and agencies are now statutorily obligated to generate and retain.

Tex. H.B. 149 (2025) (TRAIGA); see also Ropes & Gray, Navigating TRAIGA: Texas's New AI Compliance Framework (July 2025); Latham & Watkins, Texas Signs Responsible AI Governance Act Into Law. 20

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A brief word on the statutory landscape is worth adding, since it will only grow more relevant between now and the seminar date. At the federal level, Executive Order 14110, Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, directed the Attorney General to report on AI use across sentencing, parole, bail, risk assessment, police surveillance, predictive policing, prison-management tools, and forensic analysis — the report that became the DOJ's December 2024 Final Report relied upon throughout this article.

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XI. Conclusion Artificial intelligence now sits at the center of modern policing, but its speed and apparent sophistication do not erase the constitutional limits that safeguard individual liberty. As defense counsel, we must insist on transparency, validation, and accountability wherever AI tools influence searches, identifications, or evidentiary decisions. The accelerating use of facial recognition, ALPRs, digital-forensic automation, and AI-generated reports demands rigorous litigation through Kelly/Daubert hearings, Franks challenges, and motions to suppress rooted in Fourth Amendment principles — reinforced, where the facts support it, by hearsay, Confrontation Clause, due process, and equal protection arguments. Ultimately, protecting our clients requires treating AI not as an infallible authority, but as a fallible and often biased technology that must be tested, challenged, and constrained at every stage of the criminal process.

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EXAMPLE 1

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Black man, father of 10, loses job and custody of children after AI wrongly identifies him as car thief Story by Front Page Detective Staff • 8h •

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Black Man, Father of 10, Loses Job And Custody of Children After AI Wrongly Identifies Him As Car Thief

A North Carolina man spent nearly three months in jail after being wrongfully detained for a crime he says he did not commit.

Jalil Richardson was accused of stealing a car in Jacksonville, Florida, despite being nearly 400 miles away at the time and never having visited the state. The mistaken arrest stemmed in part from an artificial intelligence (AI)-powered facial recognition system that generated an 85% match between Richardson and a suspect seen in surveillance footage.

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According to Action News Jax, Richardson, a resident of Charlotte, spent a month in the Mecklenburg County Jail before being extradited to Florida, where he remained incarcerated in Jacksonville for another 50 days. He is now considering taking legal action.

The Mystery of the Stolen Car The case began after deputies with the Jacksonville Sheriff’s Office investigated the sale of a stolen car that was allegedly purchased in a Publix parking lot on April 2, 2025.

A man told police he had paid $30,000 for the vehicle. However, when he later attempted to register it, he discovered it had been reported stolen.

Investigators reviewed security footage from the parking lot and obtained a copy of a fake Georgia identification card allegedly used by the suspect. They then used an AI-based facial recognition system, which identified Richardson as a potential match.

Richardson’s lawyers argued that the identification was flawed and that he could not have been the person involved.

‘I Lost Everything’: Three Months Behind Bars Dismantled His Entire Life Richardson believes he was racially profiled, claiming investigators focused on him based on a vague physical description.

“I want to say racial profiling. The guy said it was a guy with dreads and a big nose, and then they picked me out of a lineup of guys that look nothing like me,” he said.

WSOC reported that the State Attorney’s Office took nearly a year to drop the charges against Richardson, which included grand theft, dealing in stolen property, and possessing fictitious identification. His charges were dropped in June 2026.

During that period, Richardson said his life fell apart. He lost his job, his home, his car, and custody of two of his 10 children. © 2026 Microsoft

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After also obtaining statements from two eyewitnesses, authorities with the Jacksonville Sheriff’s Office said they had established probable cause to arrest Richardson.

“I lost everything. … It’s overwhelming and it’s devastating and it’s outrageous,” Richardson told Action News Jax.

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Prior to his transfer from North Carolina to a Florida jail, Richardson criticized investigators for failing to verify his whereabouts.

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“There was no proper investigation done to even reach out to me or to see if I was even in Florida. He just automatically put a warrant out for my arrest. … And I sat in there for over 50 days in the most worst jail ever.”

He added, “I’m not sure how I’m gonna bounce back from this one, you know. It’s a lot. I’m just taking it one day at a time and get any help and any resources that I can.”

Sheriff’s Office Defends Investigation The Jacksonville Sheriff’s Office defended its use of facial recognition technology, emphasizing that officers did not rely solely on AI to identify Richardson.

According to the department, Richardson was also identified by the buyer of the stolen car during a photographic lineup.

“The victim spent approximately 30 minutes with the suspect in the case. With that and other information, officers were able to obtain a warrant for Mr. Richardson’s arrest,” the department said.

Officials also denied allegations that Richardson was targeted because of his race.

Meanwhile, Adam Schwartz, privacy litigation director for the Electronic Frontier Foundation, said that at least 14 Black men have been wrongfully arrested after being misidentified by facial recognition technology.

Richardson’s case is not the first to raise concerns in Florida. Another Black man, Richard Dillon, was arrested after facial recognition software known as the Face Analysis Comparison and Examination System (FACESNXT) matched an image of a suspect in a child-luring investigation to his photograph.

Dillon later filed a lawsuit against the Jacksonville Beach Police Department, alleging he was wrongfully arrested even after proving he was nearly 300 miles away when the alleged crime occurred.

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EXAMPLE 2

Tennessee This article is more than 4 months old

Tennessee grandmother jailed after AI facial recognition error links her to fraud

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Angela Lipps spent nearly six months in jail after AI software linked her to a North Dakota bank fraud case

Fargo police identified Angela Lipps as a suspect and later released her on Christmas Eve. Photograph: Google Maps

Marina Dunbar Thu 12 Mar 2026 16.29 EDT

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A Tennessee grandmother says she is trying to rebuild her life after an incident of mistaken identity by an artificial intelligence (AI) facial recognition system tied her to a North Dakota bank fraud investigation.

Angela Lipps, 50, spent nearly six months in jail after Fargo police identified her as a suspect in an organized bank fraud case using facial recognition software, according to south-east North Dakota news outlet InForum. Lipps told the outlet she had never been to North Dakota and did not commit the crimes.

Lipps, a mother of three and grandmother of five, said she has lived most of her life in north-central Tennessee. She had never been on an airplane until authorities flew her to North Dakota last year to face charges.

In July, US marshals arrested Lipps at her Tennessee home while she was babysitting four children. She said she was taken away at gunpoint and booked into a county jail as a fugitive from justice from North Dakota.

“I’ve never been to North Dakota, I don’t know anyone from North Dakota,” Lipps told WDAY News.

She remained in a Tennessee jail for nearly four months without bail while awaiting extradition. She was charged with four counts of unauthorized use of personal identifying information and four counts of theft.

According to Fargo police records obtained by WDAY News, detectives investigating bank fraud cases in April and May 2025 reviewed surveillance video of a woman using a fake US army military ID to withdraw tens of thousands of dollars.

The officers allegedly used facial recognition software to identify the suspect as Lipps. A detective reportedly wrote in court documents that Lipps appeared to match the suspect based on facial features, body type and hairstyle.

Lipps told WDAY News that no one from the Fargo police department contacted her before the arrest.

Her attorney, Jay Greenwood, told the outlet: “If the only thing you have is facial recognition, I might want to dig a little deeper.”

Lipps was later released on Christmas Eve after Greenwood obtained her bank records and presented them to investigators. The records showed Lipps was more than 1,200 miles away in Tennessee at the time investigators said the fraud occurred in Fargo.

But Lipps said Fargo police did not pay for her trip home, leaving her stranded. Local defense attorneys helped cover a hotel room and food on Christmas Eve and Christmas Day, and a local non-profit, the F5 Project, was able to help her return to Tennessee, InForum reported.

Lipps is now back home but says the experience has had lasting consequences. While jailed and unable to pay bills, Lipps lost her home, her car and her dog, she said. She also told WDAY News no one from the Fargo police department had apologized.

This is far from the first case of an AI error flagging the wrong suspect. In October, an AI system apparently mistook a Baltimore high school student’s bag of Doritos for a firearm and called local police to tell them the pupil was armed. Taki Allen was sitting with friends outside the Kenwood high school in Baltimore when police officers with guns approached him, made him get on his knees, and handcuffed and searched him – finding nothing.

Earlier this year, police arrested a man in the UK for a burglary in a city he had never visited after face-scanning software confused him with another person of south Asian heritage. Authorities had used automated facial recognition software which matched him with footage of a suspect in a £3,000 burglary 100 miles away.

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Authorities in North Dakota did not transport Lipps from Tennessee until the end of October, 108 days after her arrest, according to InForum. She appeared in a North Dakota courtroom the next day.

EXAMPLE 3

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In 2018, Brian Hofer and his younger brother were driving to visit their parents for Thanksgiving. It should have been a routine trip.

But that evening they found themselves held at gunpoint by a group of law enforcement officers.

The incident was the result of technology gone wrong. Hofer's vehicle had been flagged as stolen by an Automated License Plate Reader — ALPR — system. When he drove by, the reader alerted authorities.

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"Your life definitely is different after you have guns pointed at you," he said.

This incident is one of over a dozen cases verified by CBS News during a six-month investigation into incidents of wrongful stops and even several instances of ALPR technology being abused.

The consequences of ALPR errors can range from the inconvenient — such as mistaken toll booth charges — to the potentially dangerous, such as Hofer's armed detainment. In some instances the technology was improperly used by authorities, such as in Kansas, where law enforcement officers used license plate reader systems to stalk former partners in two separate incidents.

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When license plate readers get it wrong

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CBS News has verified more than a dozen instances where Automated License Plate Reader (ALPR) systems were involved in wrongful stops.

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The rise ofALPR systems In use since at least the late 1990s, automated license plate reader systems have advanced quickly in recent years. They now marry high-speed, high-resolution cameras with artificial intelligence to scan every license plate passing through a designated field of vision. The data is then compared against license plate numbers in databases.

Thousands of agencies use these systems daily to scan plates in real time and identify potential matches. Departments use ALPRs as a crime-fighting tool to gather evidence for investigations and reduce crime as well as for traffic compliance.

According to a survey from the Bureau of Justice Statistics, every police department overseeing more than 1 million citizens reported using the technology, as did 90% of sheriff's offices with 500 or more sworn deputies.

Law enforcement officials told CBS News that the technology has allowed them to do their job more efficiently and has helped solve crimes ranging from stolen vehicles to missing persons cases.

Pat Yoes, the national president of The National Fraternal Order of Police, an organization of hundreds of thousands of sworn law enforcement officers, said in an emailed statement to CBS News that ALPRs are "extraordinarily important in cases where there is an immediate threat to life or safety, as in an abduction or an armed threat driving to a target," adding that the information can be valuable in generating leads and closing cases.

Defending Against the Machine An automated license plate reader is seen mounted on a pole in San Francisco on June 13, 2024. The city has installed the technology to help combat crime.

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He said transparency is key. "The community should be made aware of the new technology, how it's used in the field, how it contributes to public safety, and how it addresses any privacy concerns," he said in the statement. "Technology like ALPRs is a valuable tool for many law enforcement agencies to make their communities safer."

The rise of ALPR systems comes as law enforcement agencies across the country face staffing and recruiting challenges. A 2024 survey by the International Association of Chiefs of Police, a professional association, found that U.S. agencies are operating at a nearly 10% staffing deficit. ALPR technology is one way to help fill this gap in manpower.

Why license plate readers make errors — whatwe found License plate reader errors may occur for a variety of reasons. In some cases, letters or numbers are interpreted incorrectly by the Optical Character Recognition, or OCR, software. According to the National Highway Traffic Safety Administration, common issues including glare or misaligned cameras could impact accuracy. CBS News found that mistakes are often due to a mix of machine and human or administrative errors.

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In Española, New Mexico, a 12-year-old was handcuffed after an ALPR camera misread the last number of a license plate on a vehicle driven by her older sister as a "7" instead of the "2" it actually ended with, according to a lawsuit filed against the city. A month later, in a separate incident, a 17-year-old honors student was held at gunpoint in Española on his way home from school after officers mistook his vehicle for one associated with an individual who was being sought in connection with a string of armed robberies.

In Aurora, Colorado, in 2020, a mother and her family, including her 6-year-old daughter, were pulled over at gunpoint and forced to lie face down on hot pavement. Again, ALPR technology was central to the stop. Police mistakenly flagged their Colorado license plate as matching that of a completely different vehicle from a different state — a stolen motorcycle registered in Montana.

The incident, captured on video and widely condemned, led to a $1.9 million settlement from the city in 2024.

Privacy and accuracy concerns The ACLU warns ALPR cameras could infringe on civil rights and violate the Constitution's Fourth Amendment by encouraging unreasonable searches. Despite widespread use, there is no federal legislative framework for ALPR use.

Hofer, who has been involved in privacy advocacy for over a decade, is now the executive director of Secure Justice, an organization that aims to reduce government and corporate overreach. He says manual verification is necessary to see whether ALPR "hits," or matches, are accurate. Even so, he said, such checks are insufficient because data errors could cause a plate to "match" an incorrect entry in a database.

"There are billions of scans a day in America. If there's even just a 10% error rate, that means there are so many opportunities for abuse to happen," Hofer said.

Some concerned citizens are taking action. Last year, residents of Norfolk, Virginia, filed a federal lawsuit against the city, and in Illinois, two residents have sued the Illinois State Police over ALPR systems, arguing that their use violates Fourth Amendment rights. The latter case was dismissed without prejudice in April.

Mikayla Denault contributed to this report.

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