The Office of Communications and University Relations
Long Island/Local Event
AI and ChatGPT for Seniors, presented by Tanya Tuzeo, SUNY Old
Westbury at the Great Neck Library
Great Neck Library, Neighbor Jun2
Event Details
Mon, Jun 2, 2025 at 6:30 PM
Add to calendar
159 Bayview Ave, Great Neck, NY, 11023
Understand how AI can enhance daily life, from managing tasks to improving communication. Through interactive activities and hands-on experience with tools like ChatGPT, participants will gain confidence in using AI to simplify and enrich their routines. The workshop will also address concerns people may have about its use, such as privacy, security, and the potential for bias.
Registration is required. Great Neck cardholders and residents can register online, in-person, or via phone. Non-residents are welcome as walk-ins, as space allows. For more information, please contact Great Neck Library at (516) 466- 8055 or email adultprogramming@greatnecklibrary.org.
Available online at: https://www.innovateli.com/suny-adds-old-westbury-to-statewide-ace-expansion/
Available online at: https://www.innovateli.com/no-980-ice-cream-floats-eagles-soar-and-refugees-arrive-whilesouthampton-gets-it-write-once-again/
First Published: 25th June 2025
DOI
https://doi.org/10.56367/OAG-047-11748
What does it mean to ‘know’ something in the age of AI?
Last Modified:25th July 2025
Stephanie Schneider from SUNY Old Westbury examines how Artificial Intelligence is reshaping our understanding of knowledge and challenging traditional concepts as it becomes increasingly integrated into our daily lives
When you ask Siri for the weather, consult ChatGPT for writing advice, or rely on GPS to navigate an unfamiliar city, you’re participating in a profound shift in how knowledge works in our world. These everyday interactions with Artificial Intelligence raise questions that philosophers have grappled with for centuries: What does it mean to ‘know’ something? And what happens when machines seem to know things too? Can machines possess knowledge in the same way humans do?
These aren’t merely abstract philosophical puzzles. As AI becomes increasingly integrated into our schools, workplaces, and daily lives, understanding how machine ‘knowledge’ differs from human knowledge becomes crucial for navigating our rapidly changing world.
Artificial Intelligence is reshaping our understanding of knowledge across several domains, including how we define knowledge, how we teach and learn, how we trust sources of information, and how we make decisions. Each of these areas reveals different ways in which AI challenges traditional epistemology while opening new possibilities for how we think, learn, and know.
The traditional view of knowledge
For over two thousand years, philosophers have generally agreed that knowledge requires three things: a belief must be true, you must have good reasons for believing it, and you must believe it yourself. This is called ‘justified true belief.’ When you know that Paris is the capital of France, you believe it’s true, it is true, and you have reliable sources supporting this belief.
This traditional view assumes that knowledge resides in human minds, shaped by our experiences, reasoning, and understanding. Knowledge isn’t just information; it involves comprehension, the ability to explain why something is true, and the capacity to apply that understanding in new or unfamiliar situations.
How AI challenges our understanding of knowledge
Artificial Intelligence systems operate very differently from human minds. They don’t form beliefs or develop understanding in the way we do. Instead, they process vast amounts of data, identify patterns, and make predictions based on statistical relationships. When an AI system tells you it’s going to rain tomorrow, it’s not ‘believing’ anything about the weather; it’s calculating probabilities based on atmospheric data.
This creates a fascinating puzzle. If knowledge requires belief and understanding, can AI systems ‘know’ anything? Or are they simply very sophisticated information processors that simulate knowledge without possessing it?
Consider a language model that can write poetry, answer complex questions, or even assist students in learning calculus. The AI might produce responses that demonstrate apparent knowledge, but it lacks conscious experience, intentional understanding, or the ability to reflect on what it ‘knows.’ It operates through pattern matching and statistical inference rather than comprehension.
Yet these systems can be remarkably effective at tasks we associate with knowledge and expertise. This forces us to reconsider whether our traditional definition of knowledge adequately captures all how reliable information can be generated and utilized.
The educational implications
These philosophical questions become practical concerns in educational settings, where AI is increasingly present in classrooms, online learning platforms, and assessment tools. Educational AI systems often embed assumptions about how learning works and what counts as knowledge, though these assumptions are rarely made explicit.
Many AI-powered educational platforms operate on behaviorist principles, treating learning as a process of accumulating correct responses to questions. They measure knowledge through performance on specific tasks rather than deeper understanding or the ability to transfer knowledge to new contexts. This approach can inadvertently narrow our conception of what it means to learn and know.
For instance, an AI tutoring system might help a student solve algebra problems by guiding them through step-by-step procedures. The student may achieve correct answers and even improve their test scores, but do they truly understand algebraic concepts? Have they developed the kind of mathematical thinking that allows them to approach novel problems creatively? The AI system, focused on measurable outcomes, might miss these deeper dimensions of mathematical knowledge.
The problem of the black box
One of the most significant challenges AI poses to traditional ideas about knowledge is what philosophers call the ‘black box’ problem. Many AI systems, particularly those using deep learning, operate in ways that are opaque even to their creators. They can produce accurate predictions or useful outputs, but we often cannot explain exactly how they arrived at their conclusions.
This creates an epistemological dilemma. Throughout history, the ability to provide justifications and explanations has been crucial to the establishment of knowledge claims. We expect experts to be able to explain their reasoning, and we teach students to ‘show their work.’ However, AI systems are increasingly making important decisions, such as medical diagnoses, loan approvals, or educational recommendations, through processes that we cannot fully understand or explain.
This opacity doesn’t necessarily make AI outputs wrong or useless, but it does challenge traditional notions of justified belief. How should we evaluate knowledge claims from systems whose reasoning processes we cannot access or verify?
New forms of epistemic trust
As AI systems become more prevalent and powerful, we’re developing new forms of what philosophers call ‘epistemic trust’ – trust in sources of knowledge. Just as we learn to trust certain human experts, institutions, or publications, we’re now learning to trust (or distrust) various AI systems. This trust operates differently from our confidence in human experts. When we trust a doctor’s diagnosis, we’re relying not just on their knowledge but also on their professional training, ethical commitments, and ability to explain their reasoning. With AI systems, we’re trusting complex algorithmic processes, the data used to train them, and the institutions that created and maintain them.
This shift has profound implications for education and a democratic society. If students increasingly rely on AI for information and analysis, how do we ensure they maintain the ability to think critically about sources, evaluate evidence, and develop independent judgment? How do we balance the genuine benefits of AI assistance with the need to preserve human epistemic agency – our capacity to think and know for ourselves?
References
1. Alvarado, R. (2023). AI as an epistemic technology. Sci Eng Ethics 29 (32). Springer. https://doi.org/10.1007/s11948-023-00451-3
2. Billingsley, W. (2024). The practical epistemologies of design and artificial intelligence. Science and Education. Springer.
3. Carabantes, M. (2020). Black-box artificial intelligence: An epistemological and critical analysis. AI & Society, 35, 309-317. https://doi.org/10.1007/s00146-019-00888-w
4. Cheung, K.K.C., Wong, K.C.K., & Lam, K.W. (2024). Unpacking epistemic insights of artificial intelligence in science education: A systematic review. Computers & Education, 210, 104-118.
5. Coeckelbergh, M. (2023). Democracy, epistemic agency, and AI. AI & Society. https://doi.org/10.1007/s00146023-01645-9
6. Doroudi, S. (2024). On the paradigms of learning analytics: Machine learning meets epistemology. Computers & Education, 201, 104-125. https://doi.org/10.1016/j.compedu.2023.104825
7. Ganascia, J.G. (2010). Epistemology of AI revisited in the light of the philosophy of information. Minds and Machines, 20(3), 309-329. https://doi.org/10.1007/s11023-010-9199-4
8. Moleka, P. (2025). A new epistemology of intelligence: Rethinking knowledge through nosology. Journal of Philosophical Research, 50, 45-67.
9. Schmidt, C.T.A. (2007). Artificial intelligence and learning: Epistemological perspectives. Educational Technology Research and Development, 55(4), 445-464. https://doi.org/10.1007/s11423-006-9015-6
10. Wheeler, G.R., & Pereira, L.M. (2004). Epistemology and artificial intelligence. Journal of Applied Logic, 2(4), 469-493. https://doi.org/10.1016/j.jal.2004.07.001
- Flag Day at Old Westbury _
Flag football as touched. down at Old E"Westbury College. :
The Nassau County school iis gearing up | for its inaugural season ofthewomen’s Sport, which first will be played at the club level in 2026 before transitioning to varsity the following: year, starting with a grassroots ¢campaign to recruit its first team.
“Right h now, we’ re relying on nny.
SPORTS EXTRA I=:
= athletics L Lenore Walsh told The Post.
Walsh added that the Panthers’ new = team, which was announced in April and will compete iin the Skyline Conference, is already gaining significant traction on campus.
“There were a lot of people who were. hearing it for the first time in our accepted - students’ day, and they were really excited,” she said of the program that iis still to name a head coach. However, when hey fill that void, there
game fas boomed in Nassau and Suffolk Counties.
“It seems to be very popular here on Long Island amongst the high schools and middle schools,” Walsh said — adding that programs such as Lindenhurst, PlainviewOld Bethpage, and Port Washinigtorr are on - the watch list.
The team's first tryouts will fake place in- the fall, with another wave scheduled to begin in the’spring semester as well.
Walsh stressed that the beauty of playing locally is walk-on opportunities also will be abundant.
“You don’t necessarily need to bea recruited student-athlete... andsometimes, you never know, you can geta diamond-in-the- -rough player,” Walsh said.
Metrics or Maturities
Gauging Cyber Performance
By Joel Lanz, CPA/CGMA/CITP/CFF, CISM, CISSP, CFE
Featured, March/April 2025, Columns, Technology |
Many corporate executives continually question and analyze their business practices to achieve their goals more effectively and efficiently. Not surprisingly, cybersecurity remains an item of concern. Most executives recognize that extensive investments for managing cyber risks must continue, but wonder if opportunities for efficiencies can coexist with the need to maintain tolerable risk management levels. Many use quantitative methods to assist them in evaluating cybersecurity management efforts.
Using Key Performance Indicators
In the current data-driven environment, executives appreciate using key performance indicators and metrics to describe how well the organization manages its cyber program. Numbers can be beneficial in assessing initiatives, especially ones that heavily rely on complex technologies or unfamiliar topics. The comfort of benchmarking KPIs is familiar to many executives and is a primary reason to try to do the same with cybersecurity. Some challenges do exist, however, because what constitutes an appropriate or higher risk indicator can vary by organization. For example, one organization may limit its assessment to a few metrics focused on the number of breaches or records breached. Their cybersecurity budgets are often based on an industry benchmark in which organizations invest a comparable amount in cyber as their competitors, and in some cases, even lower. Others take a broader view and are interested in effectiveness, process, and ultimate results. For these executives, the development and monitoring of KPIs represent a powerful solution, as they can monitor progress.
In cybersecurity, defining KPIs typically involves benchmarking against what other organizations do, focusing on issues of paramount importance to the executive team, and determining indicators using data or a combination of these factors. These KPIs are then presented in some scorecard presentation. These presentations could be as simple as a one-page scorecard highlighting critical considerations using a heat map (red, yellow, and green). Others use a point system to score their practices (for example, Stanford University’s can be found at https://tinyurl.com/hvdh49pk). Some limit their reporting to the extent that the organization complies with and addresses corporate policy requirements, including KPIs related to how well the organization addresses supporting programs such as cybersecurity and vendor management. Those seeking a more comprehensive metrics program to support extensive investments can review the National Institute of Standards and Technology’s (NIST) recently revised (December 2024) two-volume “Measurement Guide for Information Security.” The first volume focuses on selecting metrics, while the second focuses on designing a measurement program to support the monitoring, analysis, and reporting (https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-55v1.pdf and https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-55v2.pdf)
The ISACA Journal (https://tinyurl.com/mazmm7wn) provides a high-level summary of using KPIs for security governance. The article offers excellent considerations on the challenges of trying guidance. The author, Andrej Volchkov, identifies the following categories for which KPIs can be developed:
▪ Financial metrics Concerned with the financial impact of security controls.
▪ Maturity assessment Evaluates the state of security as a whole or in some specific domain, such as cyber-security, continuity, application security, or management.
▪ Modeling Intends to simplify analyses in the absence of accurate data.
▪ Assumption-based measurement Consists of limiting and focusing measurement efforts.
▪ Progress toward objectives Consists of establishing KPIs to measure the degree of progress toward a set of given objectives.
▪ Operational metrics Articulates the effectiveness of security controls and processes.
▪ Cost analysis Provides relevant indicators for security spending governance.
▪ Benchmarking Makes it possible to compare the process in place with those of other organizations or best practices.
From a risk manager’s perspective, risk metrics can result in benefits and challenges for the organization. On the one hand, metrics can be used to obtain needed program funding and justify current investments. Metrics can also be helpful for outside parties, including insurance carriers, as well as to demonstrate due diligence for
June 2025
regulators and attorneys. Metrics can also demonstrate program strength to potential merger or acquisition suitors.
Risk managers face several challenges, the most prevalent being choosing relevant metrics. Generally, many risk managers would agree that monitoring outstanding vulnerability remediation efforts is critical. Yet statistics used to show activity (e.g., the cost of individual attacks prevented) may not be appreciated, given the time and effort needed to gather and prepare the information for what some believe are bureaucratic purposes used to justify the investment in required security from business leaders. Others believe cybersecurity program effectiveness cannot be assessed in a number or series of numbers comparable to the analysis performed to determine the organization’s financial performance.
Maturity Assessment Provides a Popular and Balanced Approach
Of the categories identified above by Volchkov, maturity assessment represents a popular approach to assessing and reporting on the effectiveness of a cybersecurity program. The Software Engineering Institute (SEI), a government-supported project at Carnegie Mellon University, developed maturity guidelines to assess the software development process and quality. Eventually, they expanded these processes and methodologies into technical security areas. Through its COBIT information technology framework, ISACA introduced maturity models to the audit and accounting communities. Reputable consulting firms such as Gartner and Forrester also leveraged these models in their assessment and related benchmarking tools provided to clients.
Eventually, the NIST adopted a maturity model (four tiers) for its widely adapted Cybersecurity Framework (https://tinyurl.com/4h8548xv). Per the framework, “the Tiers describe a progression from informal, ad hoc responses to agile, risk-informed approaches and continuously improving. Selecting Tiers helps set the overall tone for how an organization will manage its cybersecurity risks” (p. 7). The four tiers (from worst to best) are Partial, Risk Informed, Repeatable, and Adaptive. The tier-level approach provided a practical scale and, in some cases, granularity for non-technical users (e.g., executives) to appreciate how their organization manages cyber risk.
Executives must confirm the maturity scale used when overseeing or assessing cyber efforts based on maturity. For example, as discussed above, the CSF uses a four-tier scale. Yet COBIT 2019, ISACA’s premier IT governance framework, uses a six-tier CMMI-based process capability scheme. These tiers range, from worst to first: Incomplete, Initial, Managed, Defined, Quantitative, and Optimizing. Those responsible for governance should confirm the scale used for actual and targeted performance.
Measure What Matters Most
Each organization is different. As a result, what matters and gets measured for each is different as well. It is often said that what gets measured gets done, yet the cost of obtaining the measures should not exceed the benefits derived from using the measures. In this author’s opinion, a combination of measures focusing on boardapproved policies (metrics) and the maturity of cyber security programs provides the optimal mix for many small to mid-size organizations.
Joel Lanz, CPA, CISA, CISM, CISSP, CFE, is a lecturer at SUNY–Old Westbury and an adjunct professor at NYU-Stern School of Business, New York, N.Y. He provides infosec advisory services through Joel Lanz, CPA, P.C., Jericho, N.Y. He is a member of The CPA Journal Editorial Advisory Board.
This article is availableonline at https://riverheadlocal.com/2025/07/15/riverhead-school-board-fills-vacant-seat-witheducation-professor-julio-gonzalez/
Article can be found onlineat: https://www.longislandpress.com/2025/07/15/bethpage-laura-gallagher/
Childbirth is still too dangerous. This ancient profession can help.
Midwives are making a big comeback, and solving a crisis in maternal health care.
ByCaitlin Carlson
The long history of midwives
July 23, 2025
It’s 5 a.m. at Mother Health International (MHI), a birth center in Northern Uganda affiliated with Yale University, when the call comes in from a local midwife about 12 miles away. A woman in her village has gone into labor. The alert sets a series of gears into motion: a motorcycle driver is immediately dispatched to zip down the mostly unpaved roads to her location, while the nurse midwives at MHI prepare a room for labor and delivery. Within an hour, the laboring mother and her local midwife arrive at MHI where she’s greeted by nurse midwives who work together to ensure a safe delivery. An ambulance stands ready in case the birth becomes difficult, but it isn’t needed. Soon, the room celebrates: a new baby is welcomed into the world and placed onto her mother’s chest.
Midwife Rachel Zaslow, the executive director of MHI, has been in this situation many times. During her nearly 20 years at the center, Zaslow has seen the near-miraculous transformation in care for Uganda’s pregnant women. When she arrived in 2006, up to 30 women might deliver babies daily in a worn-torn hospital with a single midwife. Now, MHI’s highly effective model, called the Framework for Quality Maternal and Newborn Care, facilitates collaboration between traditional midwives in local communities and certified nurse midwives. The results are impressive.
Healthy and safe births are commonplace at MHI, which has assisted with over 20,000 births since 2007. Zaslow says they have never lost a mother. That, she adds, is “extremely rare” in East Africa, and the rest of the world. Since implementing their model, the maternal mortality rate has dropped significantly, representing over 60 maternal lives saved.
According to Keisha L. Goode, PhD, Assistant Professor, Sociology at SUNY Old Westbury, anti-Black racism is “deeply intertwined with the story of midwifery.”
Could the United States, which has a maternal mortality rate much higher than other wealthy nations, benefit from MHI’s midwife-centric approach to maternal care? Research suggests that it could.
In the U.S. the overall maternal mortality rate is 18.6 per 100,000 live births; for Black women, the figure is even higher at 50.3 deaths per 100,000 live births, worse than MHI’s numbers, a statistic Zaslow describes as “alarming.” Plus, in the U.S., the maternal mortality rate has been climbing it’s already the highest among high-income countries and experts anticipate that rate to rise for a variety of reasons, including patchwork maternal care that fails many, as well as medical discrimination that disproportionally impacts Black, Native American, Hawaii and Pacific Islanders.
According to a recent article in the American Journal of Obstetrics & Gynecology, midwife care (which includes prenatal, labor and delivery, and postpartum care in settings such as hospitals and birth centers) can lower mortality rates as well as lead to fewer preterm births and low birthweight infants as well as reduced interventions, like C-sections, in labor. The authors note that although midwifery is growing, midwives attended just 10 percent of births in the U.S. in 2020.
Getting there might require a radical rethinking of maternal health in the United States. Goode notes that there are social, structural, and political determinants of health at play, all of which need to be addressed. “We need a big picture, systems re-imagination of the perinatal healthcare system,” she says. Midwifery can, as the evidence shows, be part of that shift, potentially leading to better outcomes for pregnant women and, like MIH in Uganda, significantly lowering the maternal mortality rate.
The full article can be read at https://www.nationalgeographic.com/health/article/midwife-childbirth-maternalhealth.
2 Nassau County HS students receive $5K in scholarship awards
By Bianca Rodriguez-Mora
Posted on August 25, 2025
Two Nassau County high school students received college scholarships, recognizing their achievements and future college endeavors in STEM fields.
The two received the scholarships from Career Day Inc., a Long Island-based organization that hosts school programs that connect high school students with professionals.
The students awarded were Neha Guar, a recent graduate from Hicksville High School, and Suhani Jhawar, a rising senior at Plainview Old Bethpage JFK High School.
Guar recently immigrated to the US and discovered a passion for computer science through a Career Day Inc. session. She worked towards earning the scholarship while working part-time at Walgreens to support her family. She has been awarded $1,000 and will attend Farmingdale State College in the fall.
Jhawar said that although her original intentions were to study genetic counseling, she discovered a love for occupational therapy after a Career Day Inc. session with Dr. Pam Karp of NYIT. She was awarded $4,000 and hopes to blend healthcare and creativity.
The Cattry family funded both students’ scholarships, which were intended to award students pursuing careers in STEM fields and honor academic promise, resilience, collaboration, and leadership.
The two students were honored at Career Day Inc.’s Annual Summer Soirée, held at Nassau Community College, along with past Career Day Inc. scholarship recipients August Romeo and Selin Idik.
Romeo is a recent psychology graduate from SUNY Old Westbury, and Idik is currently an FBI Explorer and legal assistant who plans to become a police officer and eventually a lawyer.
“Our students show us that when they’re seen and supported, they step into their full potential,” Beth Bucheister, executive director of Career Day Inc., said.
