Americans Would Trust AI More if Policies Ensuring Fairness Were Implemented. Here are Ten Ways to Start.
By now, you’ve probably heard that most Americans do not trust AI. This distrust is especially concerning given how deeply these systems are already shaping access to healthcare, education, housing, jobs, and public benefits. Too often, these decisions happen without transparency, oversight, or meaningful avenues for recourse. At the same time, confidence in both technology companies and government institutions to manage AI responsibly remains low.
The stakes are clear, and the policy choices we make today will make or break society’s view of AI. We are currently at a critical opportunity to shape how AI is governed before harmful practices and inequities become further entrenched. To meet this moment, the Federation of American Scientists, with the support of the Kapor Foundation, launched a policy sprint, which is an intensive, time-bound effort designed to tackle complex challenges quickly and collaboratively. Policy sprints bring together experts from across disciplines, from academics, technologists, advocates, and practitioners, to develop practical, actionable solutions.
For our SOURCE CODE AI Trust and Fairness Sprint, we’ve developed 10 memos with leading experts that are detailed, implementable policy solutions. We have delved into why fairness is so hard to define and implement, and what is needed to promote public trust in our essay that frames this new policy agenda. These memos are not exhaustive; we know the landscape of challenges and potential solutions is far broader. Instead, we offer them as a starting point: ideas that we hope will not only serve as smart and actionable tools for policymakers, but also inspire the community to build out and advance new, detailed approaches.
- Jae Yeon Kim and Aniket Kesari show how government procurement can be a powerful but underused lever to require transparency, fairness, and accountability in AI systems.
- Nicole Ozer and Brady Hirsch emphasize that in order for AI to work for the people, state leaders must adopt people-centered AI decision making frameworks. hey explain how to ask and answer foundational questions about why and whether to use AI before skipping to the how of implementation.
- JB Branch highlights similar risks in K–12 education, proposing procurement guardrails and impact assessments to protect students’ rights and safety.
- Liza Paudel advances Community Benefit Agreements as a tool for ensuring that communities, especially those disproportionately impacted, secure enforceable protections as AI-driven data center development accelerates.
- Wilneida Negrón examines the rise of “surveillance pay,” calling for stronger regulatory safeguards to protect workers from opaque, data-driven compensation systems.
- Anna Lenhart proposes embedding “Decision Subject Representatives” into AI governance, giving impacted individuals a formal role in shaping high-stakes systems.
- Y. Tony Yang focuses on Medicaid and safety-net healthcare, proposing community-centered oversight, nurse-led audits, and patient protections to ensure AI improves rather than undermines care.
- Ziwei Qi, Tatiana Lin, and Ayokunle Olagoke highlight the risks of “algorithmic invisibility” in rural communities and call for “rural AI proofing” to ensure these populations are not overlooked in AI system design, governance, and implementation.
- Jon M. Peha explores the risks of AI-generated police reports and proposes coordinated federal and local strategies that foster innovation and prevent unsafe deployments.
- Gaurav Laroia and Charlotte Slaiman argue that enforcers must look beyond standard monetary penalties to truly protect communities from algorithmic harms. They show how historic tech settlements can be transformed into an active defense, building societal resilience and preparing the public for the next wave of technological change.
To structure our policy agenda, we have considered how these ideas have employed targeted mechanisms, which we call policy levers, to ensure legitimate use, fair outcomes, and public trust in AI. These levers include government use of AI, public engagement, sectoral considerations, and redress in legal remedies; many of our policy ideas use several of them.
This work was shaped by a multidisciplinary advisory working group of 17 experts from academia, civil rights organizations, think tanks, and beyond. Their insights helped refine the project’s focus, identify the most pressing policy opportunities, and strengthen each memo through expert review. Advisory members provided input in an individual capacity; their participation does not imply endorsement of the findings or recommendations. We are grateful for their support and expertise throughout this process.
- Quinn Anex-Ries, Center for Democracy & Technology
- Alex Ault, Lawyers’ Committee for Civil Rights Under Law
- Annette Bernhardt, UC Berkeley Labor Center
- Miranda Bogen, Center for Democracy & Technology
- Leah Frazier, Lawyers’ Committee for Civil Rights Under Law
- Sanmi Koyejo, Stanford University
- Sarah Myers West, AI NOW
- Tejas N. Narechania, UC Berkeley School of Law
- Cathy O’Neil
- Asad Ramzanali, Vanderbilt Policy Accelerator
- Cody Venzke, ACLU
- Jonathan Walter, Leadership Conference on Civil and Human Rights
The ideas generated through this sprint are practical, actionable, and grounded in existing authority. They demonstrate that policymakers already have many of the tools needed to govern AI effectively; they simply need to be deployed with urgency and intention. There is a narrowing window to shape the integration of AI systems into society. With thoughtful policy action, it is still possible to build systems that are fair, transparent, and accountable, and to earn the public trust that will ultimately determine AI’s future. We hope policymakers are ready to act.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.