Federal AI rules cover some sectors more than their risks warrant
Mapping U.S. Federal AI Governance Against Sector Vulnerability
Computers and SocietyArtificial Intelligence
Summary
Different areas of the U.S. economy face varying levels of risk from artificial intelligence, but government rules don't cover all sectors equally. The authors looked at nearly 700 federal AI policy documents and found that some risks and industries get a lot of attention, while others that experts say are more vulnerable don't get much. For example, government and national security sectors are well covered, but healthcare and finance, seen as high-risk, are less addressed. This mismatch could create gaps where AI risks are not properly managed.
What this means in practice
- •For policy makers: Identify gaps in federal AI regulations where sectors rated as high risk lack sufficient governance attention.
- •For corporate risk managers: Align corporate AI risk strategies with sectors identified as under-covered in federal AI governance but vulnerable to AI risks.
Authors
Ho Ting Hung, Angelica Chowdhury, James Teague, Simon Mylius, Spencer Michaels, Peter Slattery, Alexander Saeri, Neil Thompson
Abstract
Artificial intelligence (AI) poses different levels of risk across sectors, but are these differences reflected in U.S. federal AI governance? To help answer this question, we assess 684 federal AI governance documents for their coverage of 14 sectors and 24 AI risks. We measure coverage as breadth (i.e., how frequently the risk or sector is addressed across documents) and depth (i.e., how substantively the risk or sector is discussed). We then compare sector coverage patterns for each of the 24 risks with vulnerability assessments from a Delphi study of 272 experts. Our analysis finds substantial variation in coverage: AI risks related to robustness, system security, and governance receive more attention than socioeconomic, environmental, and emerging risks, including multi-agent risks. Public administration, national security, information, and scientific services receive comparatively high levels of coverage relative to other sectors, such as finance and healthcare, which experts rate as highly vulnerable to AI risks. By mapping current coverage and identifying where it differs from expert assessments of vulnerability, we surface potential AI governance gaps which may help inform AI risk-related decisions across government and industry.