{"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":"2026-09-12"}
{"rows":[{"ev_id":"30.05.00","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Category","risk_category":"Explainability & Reasoning","risk_subcategory":null,"description":"The ability to explain the outputs to users and reason correctly","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"30.05.01","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Explainability & Reasoning","risk_subcategory":"Lack of Interpretability","description":"Due to the black box nature of most machine learning models, users typically are not able to understand the reasoning behind the model decisions","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"}]}