A Fair Artificial Intelligence Research & Regulation (FAIRR) Bureau
Summary
Artificial intelligence (AI) is transforming our everyday reality, and it has the potential to save or to cost lives. Innovation is advancing at a breakneck pace, with technology developers engaging in de facto policy-setting through their decisions about the use of data and the embedded bias in their algorithms. Policymakers must keep up. Otherwise, by ceding decision-making authority to technology companies, we face the rising threat of becoming a technocracy. Given the potential benefits and threats of AI to US national security, economy, health, and beyond, a comprehensive and independent agency is needed to lead research, anticipate challenges posed by AI, and make policy recommendations in response. The Biden-Harris Administration should create the Fair Artificial Intelligence Research & Regulation (FAIRR) Bureau, which will bring together experts in technology, human behavior, and public policy from all sectors – public, private, nonprofit, and academic – to research and develop policies that enable the United States to leverage AI as a positive force for national security, economic growth, and equity. The FAIRR Bureau will adopt the interdisciplinary, evidence-based approach to AI regulation and policy needed to address this unprecedented challenge.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.