{"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":"2026-09-11"}
{"rows":[{"ev_id":"65.04.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Fairness) ","risk_subcategory":"Data bias","description":"\"Historical and societal biases that are present in the data are used to train and fine-tune the model.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"65.19.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Fairness)","risk_subcategory":"Output bias ","description":"\"Generated content might unfairly represent certain groups or individuals.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"65.19.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Fairness)","risk_subcategory":"Decision bias ","description":"\"Decision bias occurs when one group is unfairly advantaged over another due to decisions of the model. This might be caused by biases in the data and also amplified as a result of the model’s training.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"}]}