Explore the Frameworks in the AI Risk Mitigation Database
APS governance practitioners mapping AI risks can use this consolidated reference to benchmark their own taxonomies against global frameworks—without duplicating the evidence scan work.
Key points
- MIT AI Risk Repository has published a transparent reference deck covering 13 AI risk mitigation frameworks from academic, industry, and policy sources.
- The resource is designed to help researchers, policymakers, and governance professionals compare and build on existing AI risk taxonomies.
- Early-stage resource with only 13 source documents; a fuller systematic review of mitigation frameworks is still underway.
Summary
MIT's AI Risk Repository has released a publicly accessible Google Slides deck listing the 13 documents underpinning its draft AI Risk Mitigation Taxonomy, along with key excerpts, diagrams, and source details. The resource is intended to support transparency and enable researchers, policymakers, and governance professionals to examine source frameworks, compare classifications, and build on the taxonomy for their own reviews or audits. A broader systematic review of AI risk mitigation frameworks is ongoing. The current release is modest in scope but offers a useful comparative starting point for agencies developing or reviewing their own AI risk classification approaches.
Implications for Australian agencies
- Consider Agencies developing or reviewing AI risk frameworks could assess whether the MIT taxonomy's classifications align with or usefully extend their existing risk registers.
- Monitor The systematic review currently underway may produce a more comprehensive taxonomy worth incorporating into future APS AI governance guidance.
Implications are AI-generated. Starting points, not advice.
"Explore the Frameworks in the AI Risk Mitigation Database" Source: MIT AI Risk Repository – Blog Published: 29 August 2025 URL: https://airisk.mit.edu/blog/explore-frameworks-in-the-ai-risk-mitigation-database MIT's AI Risk Repository has released a publicly accessible Google Slides deck listing the 13 documents underpinning its draft AI Risk Mitigation Taxonomy, along with key excerpts, diagrams, and source details. The resource is intended to support transparency and enable researchers, policymakers, and governance professionals to examine source frameworks, compare classifications, and build on the taxonomy for their own reviews or audits. A broader systematic review of AI risk mitigation frameworks is ongoing. The current release is modest in scope but offers a useful comparative starting point for agencies developing or reviewing their own AI risk classification approaches. Implications for Australian agencies: - [Consider] Agencies developing or reviewing AI risk frameworks could assess whether the MIT taxonomy's classifications align with or usefully extend their existing risk registers. - [Monitor] The systematic review currently underway may produce a more comprehensive taxonomy worth incorporating into future APS AI governance guidance. Retrieved from SIMS, 18 May 2026.