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AI Governance and Lessons Learned as an AI Policy Advisor in the United States Senate

AAAI 2025 · Large language models › AI and society · approach: Human-centered trust and agency · 0 citations (OpenAlex)

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An AAAI-25 invited talk by a former AAAS Fellow AI Policy Advisor in the U.S. Senate that surveys US federal AI regulation, technology-agnostic laws, and legislative gaps, and draws implications for AI researchers.

Problem

Understanding how US legislation, federal regulators, and the judiciary govern AI, and where existing consumer-protection and financial laws leave oversight gaps requiring legislative action.

Approach

Argues that US law is largely technology-agnostic, so many existing statutes (e.g., the Equal Credit Opportunity Act) already constrain AI, and identifies specific oversight gaps such as missing third-party vendor authority at some financial regulators.

Key results

• Reports that of the seven US financial regulators, only five have third-party vendor authority, with the Federal Housing Finance Agency lacking it.
• Argues the Equal Credit Opportunity Act of 1974 effectively precludes use of deep learning-based credit decision support despite never mentioning AI.

Surprising findings

• The Equal Credit Opportunity Act, though not mentioning AI, effectively constrains deep learning-based credit decision systems because adverse actions must be explained.
• A common misconception is that AI is unregulated in the US; the speaker argues many technology-agnostic laws already apply.

Limitations

• Talk abstract only; no empirical evaluation or quantitative results beyond cited legal facts.
• Single-speaker perspective based on personal experience in one Senate committee.

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