{"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":"17.03.03","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Misinformation Harms ","risk_subcategory":"Leading users to perform unethical or illegal actions","description":"\"Where a LM prediction endorses unethical or harmful views or behaviours, it may motivate the user to perform harmful actions that they may otherwise not have performed. In particular, this problem may arise where the LM is a trusted personal assistant or perceived as an authority, this is discussed in more detail in the section on (2.5 Human-Computer Interaction Harms). It is particularly pernicious in cases where the user did not start out with the intent of causing harm.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":5,"subdomain":"5.1"},{"ev_id":"17.05.00","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Category","risk_category":"Human-Computer Interaction Harms ","risk_subcategory":null,"description":"\"Harms that arise from users overly trusting the language model, or treating it as human-like\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":5,"subdomain":"5.1"},{"ev_id":"17.05.01","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Human-Computer Interaction Harms ","risk_subcategory":"Anthropomorphising systems can lead to overreliance or unsafe use ","description":"\"...humans interacting with conversational agents may come to think of these agents as human-like. Anthropomorphising LMs may inflate users’ estimates of the conversational agent’s competencies...As a result, they may place undue confidence, trust, or expectations in these agents...This can result in different risks of harm, for example when human users rely on conversational agents in domains where this may cause knock-on harms, such as requesting psychotherapy...Anthropomorphisation may amplify risks of users yielding effective control by coming to trust conversational agents “blindly”. Wher","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":5,"subdomain":"5.1"},{"ev_id":"17.05.02","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Human-Computer Interaction Harms ","risk_subcategory":"Creating avenues for exploiting user trust, nudging or manipulation ","description":"\"In conversation, users may reveal private information that would otherwise be difficult to access, such as thoughts, opinions, or emotions. Capturing such information may enable downstream applications that violate privacy rights or cause harm to users, such as via surveillance or the creation of addictive applications.\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":5,"subdomain":"5.1"}]}