Mapping U.S. Federal AI Governance Against Sector Vulnerability
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 recei...
| Authors | Ho Ting Hung, Angelica Chowdhury, James Teague et al. |
|---|---|
| Primary category | cs.CY |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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