{"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":"16.02.00","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Category","risk_category":"Risk area 2: Information Hazards","risk_subcategory":null,"description":"\"LM predictions that convey true information may give rise to information hazards, whereby the dissemination of private or sensitive information can cause harm [27]. Information hazards can cause harm at the point of use, even with no mistake of the technology user. For example, revealing trade secrets can damage a business, revealing a health diagnosis can cause emotional distress, and revealing private data can violate a person’s rights. Information hazards arise from the LM providing private data or sensitive information that is present in, or can be inferred from, training data. Observed r","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":2,"subdomain":"2.1"},{"ev_id":"16.02.01","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 2: Information Hazards","risk_subcategory":"Compromising privacy by leaking sensitive information","description":"\"A LM can “remember” and leak private data, if such information is present in training data, causing privacy violations [34].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":2,"subdomain":"2.1"},{"ev_id":"16.02.02","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 2: Information Hazards","risk_subcategory":"Compromising privacy or security by correctly inferring sensitive information ","description":"Anticipated risk: \"Privacy violations may occur at inference time even without an individual’s data being present in the training corpus. Insofar as LMs can be used to improve the accuracy of inferences on protected traits such as the sexual orientation, gender, or religiousness of the person providing the input prompt, they may facilitate the creation of detailed profiles of individuals comprising true and sensitive information without the knowledge or consent of the individual.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":2,"subdomain":"2.1"}]}