{"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":"30.03.01","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Fairness","risk_subcategory":"Injustice","description":"In the context of LLM outputs, we want to make sure the suggested or completed texts are indistinguishable in nature for two involved individuals (in the prompt) with the same relevant profiles but might come from different groups (where the group attribute is regarded as being irrelevant in this context)","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.03.02","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Fairness","risk_subcategory":"Stereotype Bias","description":"LLMs must not exhibit or highlight any stereotypes in the generated text. Pretrained LLMs tend to pick up stereotype biases persisting in crowdsourced data and further amplify them","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.03.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Fairness","risk_subcategory":"Preference Bias","description":"LLMs are exposed to vast groups of people, and their political biases may pose a risk of manipulation of socio-political processes","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.07.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Robustness","risk_subcategory":"Interventional Effect","description":"existing disparities in data among different user groups might create differentiated experiences when users interact with an algorithmic system (e.g. a recommendation system), which will further reinforce the bias","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"}]}