Generating Harms: Generative AI’s Impact and Paths Forward
A structured harm taxonomy with documented real-world examples gives APS risk and governance teams a ready reference for AI harm classification work.
Key points
- EPIC's 2023 framework identifies nine harm categories from generative AI, from physical injury to dignitary harm.
- The MIT AI Risk Repository has catalogued this as its 31st AI risk framework - a growing reference library for governance practitioners.
- Framework is US-origin and advocacy-driven; useful for taxonomy comparison but not directly calibrated to Australian regulatory context.
Implications for Australian agencies
- Consider AI governance and risk teams could compare EPIC's nine-category harm taxonomy against their agency's existing AI risk registers or harm assessment frameworks to identify gaps.
- Monitor Teams tracking international AI risk classification approaches may want to note the MIT AI Risk Repository as a growing reference library of curated frameworks.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 23 February 2026
"Generating Harms: Generative AI’s Impact and Paths Forward"
Source: MIT AI Risk Repository – Blog
Published: 24 February 2026
URL: https://airisk.mit.edu/blog/generating-harms-generative-ais-impact-and-paths-forward
The MIT AI Risk Repository has spotlighted EPIC's 2023 paper 'Generating Harms', which maps nine categories of harm from generative AI - spanning physical, economic, reputational, psychological, autonomy, discrimination, relationship, opportunity, and dignitary harms - alongside documented real-world cases including deepfakes, defamation, misinformation, and data breaches. The paper also surveys legal, regulatory, and industry interventions. This is a secondary summary from the MIT blog; the underlying EPIC paper is the substantive source. The taxonomy is broadly applicable but originates from a US privacy advocacy perspective and is not calibrated to Australian frameworks such as the APS Responsible AI Policy.
Implications for Australian agencies:
- [Consider] AI governance and risk teams could compare EPIC's nine-category harm taxonomy against their agency's existing AI risk registers or harm assessment frameworks to identify gaps.
- [Monitor] Teams tracking international AI risk classification approaches may want to note the MIT AI Risk Repository as a growing reference library of curated frameworks.
Retrieved from SIMS, 18 July 2026.