Generating Harms: Generative AI’s Impact and Paths Forward
A structured harm taxonomy from a credible US privacy advocacy centre offers a ready reference for APS risk assessment frameworks - though it predates many 2024-25 developments.
Key points
- EPIC's 2023 paper, spotlighted by MIT AI Risk Repository, maps nine harm categories from generative AI adoption.
- The taxonomy covers physical, economic, psychological, discriminatory, and dignitary harms with documented real-world examples.
- This is a secondary spotlight of an existing 2023 advocacy paper; not new primary research or Australian-specific guidance.
Summary
The MIT AI Risk Repository has spotlighted the Electronic Privacy Information Center's 2023 paper 'Generating Harms', which presents a taxonomy of nine harm categories from generative AI: physical, economic, reputational, psychological, autonomy, discrimination, relationship, loss of opportunity, and dignitary. The paper draws on major AI harm taxonomies, provides real-world case studies covering deepfakes, misinformation, data breaches, and labour disputes, and discusses regulatory and industry interventions. The MIT blog post is a summary of an existing framework rather than new research, and the underlying paper reflects the policy landscape as of 2023.
Implications for Australian agencies
- Consider APS risk and governance teams could assess whether EPIC's nine-category harm taxonomy maps usefully onto agency AI risk registers or existing harm assessment frameworks.
- Monitor Teams tracking the MIT AI Risk Repository may want to monitor which of its 31+ catalogued frameworks are most cited in regulatory discussions, as this signals emerging international consensus on harm typologies.
Implications are AI-generated. Starting points, not advice.
"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 the Electronic Privacy Information Center's 2023 paper 'Generating Harms', which presents a taxonomy of nine harm categories from generative AI: physical, economic, reputational, psychological, autonomy, discrimination, relationship, loss of opportunity, and dignitary. The paper draws on major AI harm taxonomies, provides real-world case studies covering deepfakes, misinformation, data breaches, and labour disputes, and discusses regulatory and industry interventions. The MIT blog post is a summary of an existing framework rather than new research, and the underlying paper reflects the policy landscape as of 2023. Implications for Australian agencies: - [Consider] APS risk and governance teams could assess whether EPIC's nine-category harm taxonomy maps usefully onto agency AI risk registers or existing harm assessment frameworks. - [Monitor] Teams tracking the MIT AI Risk Repository may want to monitor which of its 31+ catalogued frameworks are most cited in regulatory discussions, as this signals emerging international consensus on harm typologies. Retrieved from SIMS, 18 May 2026.