Knowledge Resource
Research Summary: Mapping U.S. Federal AI Governance Against Sector Vulnerability
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 16 September 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
Recent research maps U.S. federal AI governance documents against sector-specific vulnerabilities, finding varied coverage of artificial intelligence (AI) risks and sectors. The analysis of 684 federal documents reveals that certain AI risks, such as robustness, system security, and governance, receive greater attention, while socioeconomic and environmental risks are less covered. This suggests potential misalignment between existing governance frameworks and expert-identified vulnerabilities.
Why it matters
This analysis highlights potential gaps in existing governance frameworks, indicating that federal AI policy may not adequately address all areas of expert-identified vulnerability. Understanding these discrepancies is crucial for developing more comprehensive and effective strategies to mitigate risks and ensure responsible AI development across diverse sectors.
Key insights
- U.S. federal AI governance documents were assessed for their coverage of 14 sectors and 24 AI risks.
- Coverage was measured by breadth (frequency across documents) and depth (substantive discussion).
- Expert vulnerability assessments (via a Delphi study of 272 experts) were used for comparison.
- Substantial variation exists in the coverage of AI risks across federal documents.
- Risks related to robustness, system security, and governance receive more attention in federal AI governance.
- Socioeconomic and environmental AI risks receive comparatively less attention.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2609.16260
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- Verification ID
- ASA-EXE-2026-00572
- Version
- v1.0 · r0
- Issued
- 16 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Mapping U.S. Federal AI Governance Against Sector Vulnerability
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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