Explore the Frameworks Behind the AI Risk Repository
A transparent, citable compilation of 65 AI risk frameworks gives APS governance teams a ready reference for grounding agency-level risk taxonomies.
Key points
- MIT AI Risk Repository v3 now publicly documents all 65 source frameworks behind its AI risk taxonomy.
- The resource supports policymakers and governance professionals in auditing and building AI risk classifications.
- Directly useful for APS teams developing or reviewing AI risk frameworks and governance documentation.
Summary
MIT's AI Risk Repository has released a companion resource for Version 3 (April 2025) that publicly documents all 65 frameworks and documents underpinning its AI risk taxonomy. Presented as a navigable Google Slides deck, it includes key excerpts, diagrams, taxonomies, and citation details from academic, industry, and policy sources. The resource is designed to support researchers, policymakers, and governance professionals in examining source frameworks, building on existing taxonomies, and conducting their own audits. The underlying methodology is detailed in a peer-reviewed preprint on arXiv.
Implications for Australian agencies
- Consider APS teams developing or reviewing AI risk frameworks could use the Repository's 65-source compilation as a reference base to benchmark their own taxonomy against established classifications.
- Monitor Governance and policy teams may want to monitor future Repository versions for emerging risk categories that could inform updates to agency-level AI risk registers.
Implications are AI-generated. Starting points, not advice.
"Explore the Frameworks Behind the AI Risk Repository" Source: MIT AI Risk Repository – Blog Published: 4 April 2025 URL: https://airisk.mit.edu/blog/explore-the-frameworks-behind-the-ai-risk-repository MIT's AI Risk Repository has released a companion resource for Version 3 (April 2025) that publicly documents all 65 frameworks and documents underpinning its AI risk taxonomy. Presented as a navigable Google Slides deck, it includes key excerpts, diagrams, taxonomies, and citation details from academic, industry, and policy sources. The resource is designed to support researchers, policymakers, and governance professionals in examining source frameworks, building on existing taxonomies, and conducting their own audits. The underlying methodology is detailed in a peer-reviewed preprint on arXiv. Implications for Australian agencies: - [Consider] APS teams developing or reviewing AI risk frameworks could use the Repository's 65-source compilation as a reference base to benchmark their own taxonomy against established classifications. - [Monitor] Governance and policy teams may want to monitor future Repository versions for emerging risk categories that could inform updates to agency-level AI risk registers. Retrieved from SIMS, 18 May 2026.