{"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":"38.03.00","quick_ref":"Kumar2023","paper_title":"Ethical Issues in the Development of Artificial Intelligence: Recognizing the Risks","level":"Risk Category","risk_category":"Transparency and explainability","risk_subcategory":null,"description":"\"A recurring complaint among participants was a lack of knowledge about how AI systems made judgements. They emphasized the significance of making AI systems more visible and explainable so that people may have confidence in their outputs and hold them accountable for their activities. Because AI systems are typically opaque, making it difficult for users to understand the rationale behind their judgements, ethical concerns about AI, as well as issues of transparency and explainability, arise. This lack of understanding can generate suspicion and reluctance to adopt AI technology, as well as m","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":7,"subdomain":"7.4"},{"ev_id":"38.05.00","quick_ref":"Kumar2023","paper_title":"Ethical Issues in the Development of Artificial Intelligence: Recognizing the Risks","level":"Risk Category","risk_category":"Trust and reliability","risk_subcategory":null,"description":"\"The participants of the study emphasized the importance of trustworthiness and reliability in AI systems. The authors emphasized the importance of preserving precision and objectivity in the outcomes produced by AI systems, while also ensuring transparency in their decision-making procedures. The significance of reliability and credibility in AI systems is escalating in tandem with the proliferation of these technologies across diverse domains of society. This underscores the importance of ensuring user confidence. The concern regarding the dependability of AI systems and their inherent biase","entity":"AI","intent":"Other","timing":"Post-deployment","domain":7,"subdomain":"7.4"}]}