June 2026
April 2026
Coded Harm: AI’s Impact on Black
Center for Policy Analysis and Research Technology and Health Equity
Daniel Ikem, Technology and AI Research Fellow
In 2025, the Congressional Black Caucus Foundation’s (CBCF) Center for Policy Analysis and Research (CPAR) published a large-scale national survey of the digital lives of Black emerging adults (those ages 18 to 25).1 Its findings show a problem that current federal policy is not fully equipped to address.
Social media has become a de facto mental health infrastructure for Black young people, filling a gap left by a health care system that has not delivered culturally competent care.1,2,3 But the same platforms providing access are also generating harm at scale, through algorithmic systems that increase racial discrimination, surface mental health misinformation, and exploit developmental vulnerability through addictive design.1,4 Federal regulators have not yet adopted comprehensive algorithmic accountability rules, leaving platform design choices that affect tens of millions of young users under regulated at the federal level and inconsistently addressed across the states.5 On the judicial side, two March 2026 jury verdicts have opened a parallel litigation front, with juries finding that platform design choices can give rise to negligence and product liability claims.6,7 But neither track, legislative nor judicial, has yet developed the race disaggregated evidentiary base or the targeted accountability mechanisms that the data on Black youth would require.1,4,8 The result is a public health emergency hidden within a technology policy problem, one that Congress, federal regulators, and state attorneys general have only begun to address.1,4,7,9
Black teens and young adults use social media at substantially higher rates than their peers. In 2025, 82% of Black teens reported using Instagram, compared with 55% of White teens, with similar gaps on TikTok and X.10,11 Twenty-eight percent of Black teens reported using TikTok “almost constantly,” compared with 8% of White teens.11 The pattern extends across the broader emerging-adult age range. In a 2024 Hopelab and Common Sense Media survey of young people ages 14 to 22, 37% of Black respondents reported being on social media almost constantly, compared with 19% of their White peers.4 For many Black emerging adults, social media is not one source of health information among many; it is the primary source.1,4,5
The centrality of social media matters because of what Black young people are looking for. The CPAR survey found that 47% of Black emerging adults searched for online content related to depression, 47% for anxiety, and 46% for stress.1 The pattern reflects systemic failure rather than preference. In Kaiser Family Foundation (KFF) survey data, 46% of Black adults who received or tried to receive mental health care reported difficulty finding a provider who understood their background and experiences, and many cited cost as a central barrier to care.2 Only about 4% of psychologists and roughly 2% of psychiatrists in the United States identify as Black, even though Black Americans make up more than one in ten U.S. residents.3 Where
the health care system has not shown up, social media has. Emerging evidence shows that platform algorithms now mediate the search for the kind of support that culturally competent providers would otherwise provide.1,4,5
Black emerging adults’ disproportionate reliance on social media for mental health information is not simply an artifact of individual user preferences or behavior. It is a function of how platform algorithms are designed, optimized, and regulated, or, in the current federal landscape, largely not regulated at all.1,4,5 Treating online wellbeing as a racial justice issue requires that federal policy recognize what the data already show: the harms of algorithmic systems are not distributed evenly across users.5 Policy frameworks that ignore racial patterning will fail the population most exposed to the harm.1,4,5
How Algorithms Compound the Crisis
A 2025 JAMA Network Open study provides some of the most direct evidence connecting algorithmic systems to Black youth mental health.12 Using daily diary methodology with 141 Black and biracial adolescents, researchers found media-based participants encountered an average of six race related online experiences per day, including an average of 3.2 instances of racial discrimination.12 Online racial discrimination, traumatic events online, and algorithmic or filter bias were all significantly associated with next day anxiety and depressive symptoms.12 Other work has found that adolescents of color who experienced social media based racism had dramatically higher odds of suicidal ideation in the same week, with several fold increases in acute suicide risk compared with those without such exposure.13
The harms documented in JAMA Network Open sit within a broader record on algorithmic discrimination against Black users. Youth exposed to social media based racism show sharply elevated risks of suicidal ideation, compounding the mental health impacts of 13
on the platforms.14 Content moderation algorithms flag tweets in African American English at up to twice the rate of Standard American English, and moderation systems disproportionately silence users who share personal experiences with racism, though guideline reframing interventions show some promise in reducing this effect.15,16
These
harms extend beyond social media. In health care, a widely used risk prediction algorithm affecting millions of patients systematically understated Black patients’ health needs; correcting the bias would nearly triple the share of Black patients identified for additional care, from about 18% to roughly 47%.17
In employment, large language models have favored resumes with White associated names in the vast majority of more than three million simulated comparisons, often choosing the White associated name roughly 80 to 85% of the time.18 In lending, Black mortgage applicants would need credit scores roughly 100 or more points higher than White applicants to receive comparable treatment from AI mortgage underwriting systems.19 Each encounter compounds the others, producing cumulative racism based traumatic stress that no individual user can offset through behavioral adaptation. Mitigation requires intervention upstream, at the level of platform design, algorithmic transparency, and the training data that underlie the systems themselves, because individual coping cannot offset structurally generated exposure.5,12,13,17–19
Within the CPAR survey of Black emerging adults, gender diverse respondents reported some of the highest levels of online distress and anxiety, consistent with broader evidence that LGBTQ+ youth of color face compounding algorithmic exposure to both racial and identity based harms.1,4 The same survey found that nearly three in four respondents had unintentionally shared false health content on social media, indicating that low quality mental health information is reaching Black users and circulating through their networks at scale, even as the underlying mechanisms of platform amplification remain opaque to researchers and users alike.1,4
The downstream mental health data underscores the urgency of algorithmic accountability. Suicide rates among Black youth ages 10 to 17 increased 144% between 2007 and 2020, the fastest growth of any racial group, as documented by the Congressional Black Caucus Emergency Taskforce’s Ring the Alarm report 20 By 2024, the suicide rate for young adults ages 18 to 25 overall reached 15.7 per 100,000, an increase of about 17% since 2014. Over the same period, suicide death rates rose fastest for Black people compared with other racial groups.21,22 Centers for Disease Control and Prevention (CDC) analysis identified suicide as the third leading cause of death for Black youth ages 10 to 24 in 2023 and documented about a 29% increase in suicide rates for that group between 2018 and 2023.9 Together, these data points show that rising suicide risk among Black youth is not an isolated phenomenon but part of a broader, accelerating crisis.9,20-22
The Federal Mental Health Response
Federal responses to Black youth mental health have proceeded on two tracks that have not yet converged. The first track addresses mental health care directly, through the workforce, research, and clinical capacity. The second track, addressed later in this brief, addresses the algorithmic systems shaping where Black young people now turn for support. Neither alone is sufficient.1,4,20
Congressional Black Caucus (CBC) members have repeatedly pushed for federal action on these disparities. Following nine months of work by the CBC Emergency Taskforce on Black Youth Suicide and Mental Health, Congresswoman Bonnie Watson Coleman (D-NJ) introduced the Pursuing Equity in Mental Health Act, which would authorize $995 million for research, workforce pipeline development, anti-stigma outreach, and provider training on bias and culturally competent care.20 The bill passed the House in 2020 and 2021 but has not yet become law; it was reintroduced in the 119th Congress as H.R. 2904, with Senator Mazie Hirono (D-HI) introducing the Senate companion bill as S. 1448, and both remain pending.23
Federal health authorities have begun to acknowledge the problem at a population level. The U.S. Surgeon General’s 2023 advisory established that teens using social media more than three hours daily face roughly double the risk of depression and anxiety symptoms, based on a synthesis of longitudinal and cross sectional studies.6 Then, in 2024, the Surgeon General publicly called for congressionally mandated warning labels on social media platforms.6 The advisory does not, however, disaggregate its findings by race.6 That omission is the policy gap. Black teens exceed the three-hour threshold at substantially higher rates than any other group, which means
Courts Are Holding Platforms Accountable
While Congress has not yet passed comprehensive algorithmic accountability legislation, courts and state attorneys general have begun to fill the vacuum. State enforcement predates the recent verdicts. In October 2023, a coalition of 33 state attorneys general led by California and Colorado filed a federal complaint against Meta, alleging that Instagram and Facebook were designed to exploit and addict youth users in violation of state consumer protection law, and an additional nine states filed parallel actions in their own courts.7 That multistate enforcement action helped establish the legal posture that platform design choices can be challenged under existing state authority, and it laid important groundwork for the jury verdicts that followed in March 2026.7
Section 230 of the Communications Decency Act of 1996 has historically protected online platforms from many lawsuits over what their users post. For nearly three decades, that protection has often foreclosed claims targeting platforms for harmful third-party content circulating on their services. The newer litigation theory seeks to work around that barrier by targeting the platforms’ own design choices, such as recommendation algorithms, autoplay features, and infinite scroll, rather than the user generated content those features distribute.6,7 Because the platforms, not their users, design these features, plaintiffs argue that Section 230’s protections do not apply in the same way. Two March 2026 verdicts have become early tests of that theory.6,7
K.G.M.
v. Meta et al. (Los Angeles, March 25, 2026)
In what news coverage and litigation summaries describe as the first U.S. civil jury verdict holding social media companies liable for addictive design, a Los Angeles jury found Meta (Instagram) and Google (YouTube) negligent for the design of their platforms.6,7 It awarded the plaintiff $6 million, comprising $3 million in compensatory damages and $3 million in punitive damages.6,7 The plaintiff, now 20, began using YouTube at age 6 and Instagram at age 9 and developed depression, body dysmorphia, and suicidal ideation that her attorneys linked to compulsive engagement driven by infinite scrolling, algorithmic recommendations, and autoplay features. The case served as a bellwether trial designed to test how jurors respond to the legal theories and evidence now appearing across a much larger field of social media youth harm litigation. approximately 1,600 similar cases consolidated in California state court for coordinated handling. settlement negotiations across thousands of related claims pending nationwide.
Subsequent litigation updates report that TikTok and Snap settled before their own scheduled trials, while Meta and Google announced appeals.24
New Mexico v. Meta (Santa Fe, March 24, 2026)
A Santa Fe jury found Meta liable in a state enforcement action alleging that the company knowingly harmed children’s mental health and concealed evidence related to child sexual exploitation on its platforms.7 The court ordered $375 million in civil penalties under the state’s Unfair Practices Act.7 Unlike K.G.M., which sought damages for a single plaintiff, the New Mexico case was brought by the state attorney general as a consumer protection enforcement action, and the penalty reflects aggregated statutory fines rather than individualized damages.7 The case demonstrates that state attorneys general can pursue platform accountability under existing state law without waiting for new federal legislation.7
Together, the verdicts indicate that platform design may be treated as a source of legal liability independent of user speech. They also suggest that at least some design-based claims may survive Section 230 objections because they target the architecture of the service rather than third party expression.6,7 More than 10,000 individual lawsuits and nearly 800 school district cases are now pending nationwide. The federal cases have been consolidated into a single proceeding in federal court in Oakland, CA, known as Multidistrict Litigation (MDL) 3047, which centralizes pretrial proceedings against Meta, TikTok, Google, and Snap.24 The first bellwether trials in that federal proceeding are expected to begin in June 2026. Those trials will likely produce the first federal verdicts testing whether platform design itself can support product liability or negligence claims, with significant implications for settlement values and litigation strategy across the larger docket.24
A systematic review of pretrial filings in major social media youth harm lawsuits found that race or ethnicity was mentioned only twice across all filings, and no filing employed an intersectional analytical framework to examine how race, gender, and platform exposure interact.25 The omission is consequential. The plaintiffs whose stories anchor the litigation are presented as generic adolescents, even though the empirical record on algorithmic harm shows clear racial patterning and even though Black youth use the platforms at issue at substantially higher rates than their peers.4,10-16 When major verdicts and penalties enter the legal record without race disaggregated evidence, the case law and settlement frameworks that follow risk becoming race blind by default.25
Plaintiffs’ counsel, civil rights organizations, and state attorneys general bringing future actions have an opening to correct this. So do amici, organizations that are not parties to a case but submit briefs because they have an institutional interest in the outcome, including CBCF and allied organizations, in the MDL 3047 federal proceedings. Amicus briefs allow civil rights organizations to place evidence and legal arguments before the court that the named plaintiffs may not present on their own. In MDL 3047, that means ensuring that the federal record reflects the disproportionate exposure of Black youth to algorithmic harm, so the case law and settlement frameworks that emerge from Oakland are not built on a race blind evidentiary base.25
The Legislative Response
Federal action on algorithmic accountability is not starting from scratch. California’s Age Appropriate Design Code Act (AB 2273), enacted in 2022, requires platforms likely to be accessed by minors to assess product risks and configure high privacy defaults for users under 18.26 The United Kingdom’s Age Appropriate Design Code, in force since 2021, establishes a similar framework at the national level.27 These models provide tested regulatory architecture that federal proposals can parallel or build upon.26,27
The federal legislative response to algorithmic harm has coalesced around three companion bills introduced or reintroduced in the 119th Congress: the Algorithmic Accountability Act, the AI Civil Rights Act, and the Eliminating Bias in Algorithmic Systems (BIAS) Act.28-30 Together, they represent a three pronged approach to AI accountability: requiring companies to assess the risks of their AI systems before deployment, allowing individuals harmed by discriminatory AI systems to sue once harm has occurred, and requiring federal agencies that use or fund AI to maintain dedicated civil rights oversight of those systems.28-30 Each includes support from members of the CBC.28-30
The Algorithmic Accountability Act of 2025 (H.R. 5511 / S. 2164), introduced by Congresswoman Yvette D. Clarke (D-NY, Chair of the Congressional Black Caucus) and Senator Ron Wyden (D-OR), would require large companies to conduct algorithmic impact assessments for automated decision systems used in critical domains including housing, employment, credit, education, and health care, and to submit those assessments to the Federal Trade Commission (FTC).28 The bill would establish a Bureau of Technology within the FTC and would treat noncompliance as an unfair or deceptive practice enforceable by the Commission.28 House cosponsors include eleven CBC members, among them Congresswomen Robin Kelly (D-IL), Yvette Clarke, Summer Lee (D-PA), and Shontel Brown (D-OH), several of whom sit on the Energy and Commerce and Oversight committees with jurisdiction over the bill. Outside supporters include the Electronic Privacy Information Center, the Center for Democracy and Technology, Color of Change, and Consumer Reports, among others.28
The AI Civil Rights Act, reintroduced on December 2, 2025, by Congresswomen Yvette Clarke, Pramila Jayapal (D-WA), Summer Lee, and Ayanna Pressley (D-MA), with Senator Ed Markey (D-MA), would prohibit algorithmic discrimination in employment, housing, credit, education, health care, and public accommodations; require pre deployment testing for disparate impact; and create a private right of action enabling individuals harmed by AI systems to sue for damages and injunctive relief. action is particularly significant for Black communities because civil rights enforcement under existing federal statutes has often depended on Department of Justice or agency action, which has been inconsistent across administrations. action allows affected individuals and civil rights organizations to bring claims directly when algorithmic systems produce discriminatory outcomes in employment, housing, lending, health care, education, or public accommodations, without waiting for federal enforcement priorities to align.29
The Eliminating BIAS Act, reintroduced on January 15, 2026, by Congresswoman Summer Lee and Senator Markey, would require every federal agency that uses, funds, or oversees AI to establish a dedicated civil rights office staffed with algorithmic accountability and civil rights enforcement expertise, and to submit regular reports to Congress on AI bias and discrimination within its jurisdiction.30
Together, the three bills form a layered accountability regime.
The Algorithmic Accountability Act addresses design by requiring companies to evaluate the risks of their AI systems and report findings to the FTC before harm occurs, creating a regulatory record that does not currently exist.28 The AI Civil Rights Act addresses harm by giving individuals and civil rights organizations the ability to sue when AI systems produce discriminatory outcomes, a meaningful development because many existing federal civil rights statutes were drafted before algorithmic decision making became widespread.
that use or fund AI to maintain in house civil rights expertise, which matters because federal agencies are major purchasers and deployers of AI systems and often lack specialized staff capacity to evaluate those systems for bias. three bills would significantly narrow the gap between AI systems and existing civil rights frameworks.
long-term limits on state level AI regulation, suggests that passage in the 119th Congress federal framework against which future state legislation and litigation may be measured, administrative and judicial advocacy, and they provide statutory text that could later be enacted in part through amendments, appropriations riders, or future legislative vehicles
Policy Recommendations
The evidence assembled in this brief points to a single conclusion: the algorithmic systems shaping where Black young people turn for mental health support are advancing faster than the legal and regulatory frameworks meant to govern them. Closing that gap will require coordinated action across Congress, federal regulators, and the courts. The five recommendations below are designed to work together, pairing new accountability requirements with the race disaggregated evidence base needed to ensure those requirements reach the population most exposed to harm.
1. Pass the Algorithmic Accountability Act. Congress should advance H.R. 5511 / S. 2164, with race, ethnicity, age, and gender disaggregation written into the statutory requirements for algorithmic impact assessments. The FTC’s proposed Bureau of Technology should be staffed with expertise in civil rights, racial equity, and community health so that enforcement reflects the populations most affected by algorithmic harm.28
2. Pass companion civil rights legislation. Congress should advance the AI Civil Rights Act and the Eliminating BIAS Act alongside the Algorithmic Accountability Act. The AI Civil Rights Act’s private right of action is particularly consequential for Black communities because it does not depend on federal agency action; affected individuals and civil rights organizations can litigate directly when algorithmic systems produce discriminatory outcomes.29,30
3. Require race disaggregated platform data through FTC rulemaking and statutory mandate. Voluntary platform commitments have not produced the data that researchers, regulators, or affected communities need to determine where harm is concentrated. Congress should authorize the FTC to require large platforms, defined by user base or revenue threshold, to publish annual transparency reports disaggregating usage patterns, harm exposure, content moderation actions, and mental health related content interactions by race, ethnicity, age, and gender. State attorneys general should be authorized as concurrent enforcers under state
4. Hold platforms legally responsible for harmful design choices. The March 2026 verdicts in California and New Mexico demonstrated that juries are willing to hold platforms accountable when their products are designed in ways that harm users. That accountability should not depend on whether a particular family or attorney general has the resources to sue. Congress should pass federal legislation that treats addictive design as a source of legal liability, recognizing that features such as infinite scroll, autoplay, and targeted recommendations to minors can create foreseeable harms. The multistate coalition led by California and Colorado, and state attorneys general bringing other pending cases, should put evidence of disproportionate harm to Black youth on the record. The federal bellwether trials in MDL 3047, expected to begin in Oakland in June 2026, will likely produce the first federal case law testing platform design liability. Amicus briefs from civil rights organizations can urge the court to weigh evidence of disproportionate harm to Black youth in shaping that case law.6,7,8,24
5. Protect children through enforceable age-appropriate design standards. Congress should enact federal age-appropriate design legislation modeled on California’s Age Appropriate Design Code Act and the United Kingdom’s Age Appropriate Design Code. The federal standard should require that platforms likely to be accessed by minors assess the risks their products pose to children, configure high privacy settings by default for users under 18, limit data sharing for advertising, restrict high risk forms of algorithmic personalization, limit push notifications, and provide robust parental controls. Enforcement should rest with the FTC under its existing unfair and deceptive practices authority, with concurrent authority for state attorneys general and, if Congress chooses, a carefully tailored private right of action for knowing or repeated violations. Annual compliance audits should be made public and should disaggregate findings by demographic impact.26,27
Conclusion
The rise in suicide and mental health distress among Black youth is a public health emergency, and the algorithmic systems that now mediate how Black young people seek support are a central and underexamined part of it. Congress, federal regulators, and state attorneys general have each begun to act, but none of the current tracks yet rest on evidence that accounts for how algorithmic harm is distributed by race. Black young people use these platforms more, rely on them more heavily for mental health information, and absorb more of their harms. Policy and litigation that fail to reflect that reality will leave the most exposed population the least protected. Building race disaggregated evidence into algorithmic accountability is what will determine whether the response to this crisis reaches the young people at its center.
References
1. Stewart, A. (2025). Black on the timeline: Social media, misinformation, and the mental health of Black emerging adults. Congressional Black Caucus Foundation, Center for Policy Analysis and Research. https://www.cbcfinc.org/publications/cpar/black-on-the-timeline-social-mediamisinformation-and-the-mental-health-of-black-emerging-adults/
2. KFF. (2025, August 13). Black and Hispanic adults with fair or poor mental health are less likely than white adults to say they received mental health services. https://www.kff.org/mental-health/
3. American Psychiatric Association. (2017, December 19). Mental health disparities: Mental health facts for African Americans. https://www.psychiatry.org/getmedia/bc6ae47f-b0aa-4418-b045952ede06757f/Mental-Health-Facts-for-African-Americans.pdf
4. Madden, M., Calvin, A., & Hasse, A. (2024, May 21). A double-edged sword: How diverse communities of young people think about the multifaceted relationship between social media and mental health. Common Sense Media & Hopelab. https://www.commonsensemedia.org/sites/ default/files/research/report/2024-double-edged-sword-hopelab-report_final-release-for-web-v2. pdf
5. Stewart, A. (2025, December 11). Social media is filling the gaps in Black mental health and creating new ones. Tech Policy Press. https://www.techpolicy.press/social-media-is-filling-the-gaps-inblack-mental-health-and-creating-new-ones/
6. Office of the Surgeon General. (2023). Social media and youth mental health: The U.S. Surgeon General’s advisory. U.S. Department of Health and Human Services. https://www.hhs.gov/sites/ default/files/sg-youth-mental-health-social-media-advisory.pdf; Murthy, V. H. (2024, June 17). Surgeon general: Why I’m calling for a warning label on social media platforms. The New York Times. https://www.nytimes.com/2024/06/17/opinion/social-media-health-warning.html
7. Allyn, B. (2026, March 25). Jury finds Meta and Google negligent in social media harms trial. NPR. https://www.npr.org/2026/03/25/nx-s1-5746125/meta-youtube-social-media-trial-verdict
8. Kerr, D. (2026, March 25). Meta and YouTube designed addictive products that harmed young people, jury finds. The Guardian https://www.theguardian.com/media/2026/mar/25/jury-verdictus-first-social-media-addiction-trial-meta-youtube
9. Stone, D. M., Ballesteros, M. F., & Mack, K. A. (2025). Notes from the field: Differences in suicide rates, by race and ethnicity and age group, United States, 2018–2023. MMWR Morbidity and Mortality Weekly Report, 74(35). https://www.cdc.gov/mmwr/volumes/74/wr/mm7435a2.htm
10. Faverio, M., & Sidoti, O. (2025, December 9). Teens, social media and AI chatbots 2025. Pew Research Center. https://www.pewresearch.org/internet/2025/12/09/teens-social-media-and-aichatbots-2025/
11. Faverio, M., & Sidoti, O. (2024, December 12). Teens, social media and technology 2024. Pew Research Center. https://www.pewresearch.org/internet/2024/12/12/teens-social-media-andtechnology-2024/
12. Tynes, B. M., McGee, T., & English, D. (2025). Adolescents’ daily race-related online experiences and mental health outcomes. JAMA Network Open, 8(10), e2536870. https://doi.org/10.1001/ jamanetworkopen.2025.36870
13. Oshin, L. A., Boyd, S. I., Jorgensen, S. L., Kleiman, E. M., & Hamilton, J. L. (2024). Exposure to racism on social media and acute suicide risk in adolescents of color. Journal of the American Academy of Child and Adolescent Psychiatry, 63(8), 757–760. https://doi.org/10.1016/j.jaac.2024.03.009
14. Painter, D. L., Bown, F., & Vielot, K. (2025). The real cancel culture: Racial bias and injustice on social media. Howard Journal of Communications, 1–21. https://doi.org/10.1080/10646175.2025.2492114
15. Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019). The risk of racial bias in hate speech detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1668–1678). https://doi.org/10.18653/v1/P19-1163
16. Lee, M. K., Gligoric, K., et al. (2024). People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise. Proceedings of the National Academy of Sciences, 121(38), e2322764121. https://doi.org/10.1073/pnas.2322764121
17. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/ science.aax2342
18. Wilson, K., et al. (2024, October 31). AI tools show biases in ranking job applicants’ names according to perceived race and gender. University of Washington. https://www.washington.edu/ news/2024/10/31/ai-bias-resume-screening-race-gender/
19. Bowen, D. J., III, et al. (2024). AI exhibits racial bias in mortgage underwriting decisions [Working paper]. Lehigh University. https://news.lehigh.edu/ai-exhibits-racial-bias-in-mortgage-underwritingdecisions
20. Congressional Black Caucus Emergency Taskforce on Black Youth Suicide and Mental Health. (2019). Ring the alarm: The crisis of Black youth suicide in America. https://watsoncoleman.house. gov/imo/media/doc/full_taskforce_report.pdf
21. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. (n.d.). WISQARS leading causes of death visualization tool. U.S. Department of Health and Human Services. Retrieved April 15, 2026, from https://wisqars.cdc.gov/lcd/
22. Saunders, H., Panchal, N., & Rudowitz, R. (2026, February 24). Suicide deaths: National trends and variation by demographics and states. KFF. https://www.kff.org/mental-health/suicide-deathsnational-trends-and-variation-by-demographics-and-states/
23. Pursuing Equity in Mental Health Act, H.R. 2904, 119th Cong. (2025). https://www.congress.gov/ bill/119th-congress/house-bill/2904; Pursuing Equity in Mental Health Act, S. 1448, 119th Cong. (2025). https://www.congress.gov/bill/119th-congress/senate-bill/1448
24. The Spencer Law Firm. (2026, March 11). Social media addiction lawsuits (2026): KGM trial, MDL 3047, and TikTok & Snapchat settlements explained. https://www.spencer-law.com/post/socialmedia-addiction-lawsuits-2026-kgm-trial-mdl-3047
25. Richards, J., Niitsu, K., & Kenworthy, N. (2025). Mental health v. social media: How US pretrial filings against social media platforms frame and leverage evidence for claims of youth mental health harms. SSM – Mental Health, 7, 100378. https://doi.org/10.1016/j.ssmmh.2024.100378
26. California Age-Appropriate Design Code Act, A.B. 2273, 2021–2022 Reg. Sess. (Cal. 2022). https:// leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202120220AB2273
27. Information Commissioner’s Office. (2020). Age appropriate design: A code of practice for online services. United Kingdom. https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/ childrens-information/childrens-code-guidance-and-resources/age-appropriate-design-a-codeof-practice-for-online-services/
28. Algorithmic Accountability Act of 2025, H.R. 5511, 119th Cong. (2025); S. 2164, 119th Cong. (2025); Clarke, Y. D. (2025, September 19). Clarke introduces bill to regulate AI’s control over critical decision making in housing, employment, education, and more [Press release]. https://clarke. house.gov/clarke-introduces-bill-to-regulate-ais-control-over-critical-decision-making-in-housingemployment-education-and-more/
29. Jayapal, P., Clarke, Y. D., Lee, S., & Markey, E. J. (2025, December 2). Jayapal, Markey, Clarke, Lee reintroduce AI Civil Rights Act [Press release]. https://jayapal.house.gov/2025/12/02/jayapalmarkey-clarke-lee-reintroduce-ai-civil-rights-act/
30. Lee, S. L. (2026, January 15). Rep. Summer Lee, Sen. Markey reintroduce legislation to mandate civil rights offices in federal agencies that manage artificial intelligence [Press release]. https:// summerlee.house.gov/newsroom/press-releases/rep-summer-lee-sen-markey-reintroducelegislation-to-mandate-civil-rights-offices-in-federal-agencies-that-manage-artificial-intelligence
June 2026
Center for Policy Analysis and Research
Acknowledgements:
Technology and AI Fellowship sponsored through partnership with American Institutes for Research Opportunity Fund