Source Code: Building an AI Trust and Fairness Policy Agenda
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.
Procurement is not merely an administrative function—it is how AI enters government and the first line of defense for responsible AI in the public sector.
Responsible AI starts with who is in the data, who is at the table, whose needs shape the outcome, and who is responsible when it falls short.