{"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":"04.01.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Toxicity and Abusive Content","risk_subcategory":null,"description":"This typically refers to rude, harmful, or inappropriate expressions.","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"04.02.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Unfairness and Discrimination","risk_subcategory":null,"description":"Social bias is an unfairly negative attitude towards a social group or individuals based on one-sided or inaccurate information, typically pertaining to widely disseminated negative stereotypes regarding gender, race, religion, etc.","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"04.03.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Ethics and Morality Issues","risk_subcategory":null,"description":"LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.","entity":"AI","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.3"},{"ev_id":"04.04.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Controversial Opinions","risk_subcategory":null,"description":"The controversial views expressed by large models are also a widely discussed concern. Bang et al. (2021) evaluated several large models and found that they occasionally express inappropriate or extremist views when discussing political top-ics. Furthermore, models like ChatGPT (OpenAI, 2022) that claim political neutrality and aim to provide objective information for users have been shown to exhibit notable left-leaning political biases in areas like economics, social policy, foreign affairs, and civil liberties.","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"04.05.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Misleading Information","risk_subcategory":null,"description":"Large models are usually susceptible to hallucination problems, sometimes yielding nonsensical or unfaithful data that results in misleading outputs.","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":3,"subdomain":"3.1"},{"ev_id":"04.06.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Privacy and Data Leakage","risk_subcategory":null,"description":"Large pre-trained models trained on internet texts might contain private information like phone numbers, email addresses, and residential addresses.","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":2,"subdomain":"2.1"},{"ev_id":"04.07.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Malicious Use and Unleashing AI Agents","risk_subcategory":null,"description":"LMs, due to their remarkable capabilities, carry the same potential for malice as other technological products. For instance, they may be used in information warfare to generate deceptive information or unlawful content, thereby having a significant impact on individuals and society. As current LMs are increasingly built as agents to accomplish user objectives, they may disregard the moral and safety guidelines if operating without adequate supervision. Instead, they may execute user commands mechanically without considering the potential damage. They might interact unpredictably with humans a","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.0"}]}