{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-27"}
{"rows":[{"ev_id":"56.01.00","slug":"discrimination-gos","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Discrimination","risk_subcategory":null,"description":"\"More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1","url":"https://aipolicytracker.org/ai-risk/risks/discrimination-gos"},{"ev_id":"56.04.00","slug":"amplification-of-biases","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Amplification of biases","risk_subcategory":null,"description":"\"Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is not limited to text generation but can be seen across all modalities of generative AI93. Training on large swathes of UK and US English internet content can mean that misogynistic, ageist, and white supremacist content is overrepresented in the training data94.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1","url":"https://aipolicytracker.org/ai-risk/risks/amplification-of-biases"}]}