Lessons from the FDA for AI
A detailed regulatory design study using FDA as analogy - useful background for APS teams developing or assessing AI assurance frameworks.
Key points
- AI Now Institute draws on FDA pharmaceutical regulation as a model for ex ante AI regulatory design.
- The report examines premarket scrutiny, regulatory functions, and industry capture risks - all live questions for Australian AI governance.
- Published mid-2024; the political climate the authors describe as hostile to premarket AI enforcement remains broadly unchanged.
Implications for Australian agencies
- Consider APS teams developing AI assurance or mandatory guardrail frameworks could consider the FDA's premarket/postmarket distinction as a structural reference point.
- Monitor Policy teams working on AI regulation design may want to monitor whether Australian or international bodies formally adopt FDA-style framing in regulatory proposals.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"Lessons from the FDA for AI"
Source: AI Now Institute – Publications
Published: 1 August 2024
URL: https://ainowinstitute.org/publications/research/lessons-from-the-fda-for-ai
This AI Now Institute report examines what AI regulators can learn from the US Food and Drug Administration's pharmaceutical oversight model. Rather than arguing for a literal 'FDA for AI', it analyses how the FDA discharges premarket and postmarket regulatory functions, distils three crosscutting lessons, and identifies four practical challenges for transplanting similar mechanisms to AI: bounding the AI market, securing adequate regulatory powers, navigating legal constraints, and avoiding industry capture. The report includes a comparative mapping of FDA regulatory mechanisms against current AI regulatory proposals, and appendices on how AI is governed today.
Implications for Australian agencies:
- [Consider] APS teams developing AI assurance or mandatory guardrail frameworks could consider the FDA's premarket/postmarket distinction as a structural reference point.
- [Monitor] Policy teams working on AI regulation design may want to monitor whether Australian or international bodies formally adopt FDA-style framing in regulatory proposals.
Retrieved from SIMS, 18 July 2026.