{"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":"2026-09-13"}
{"rows":[{"ev_id":"13.01.06","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":"Environmental Costs","description":"\"The computing power used in training, testing, and deploying generative AI systems, especially large scale systems, uses substantial energy resources and thereby contributes to the global climate crisis by emitting greenhouse gasses.\"","entity":"Human","intent":"Unintentional","timing":"Other","domain":6,"subdomain":"6.6"},{"ev_id":"13.02.05","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: People and Society","risk_subcategory":"Ecosystem and Environment","description":"\"Impacts at a high-level, from the AI ecosystem to the Earth itself, are necessarily broad but can be broken down into components for evaluation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":6,"subdomain":"6.6"}]}