A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents - Li, Megan; Bickersteth, Wendy; Tang, Ningjing; Cranor, Lorrie; Hong, Jason; Shen, Hong; Heidari, Hoda (Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2025)
Generative AI is applied in an ever-growing set of domains and tasks, leading to an expanding set of risks of harm impacting people, communities, society, and the environment. These risks may arise due to failures during the design and development of the technology, as well as during its release, deployment, or downstream usages and appropriations of its outputs. In this paper, building on prior taxonomies of AI risks, harms, and failures, we construct a taxonomy specifically for Generative AI failures and map them to the harms they’re associated with in the real world. Through a systematic analysis of 499 publicly reported incidents of harm involving Generative AI, we describe what harms are reported and how often, how they tend to arise, and who they impact. We find that most reported incidents are associated with use-related failures but the harms are experienced by parties beyond the end user(s) of the system at fault, and that the landscape of Generative AI harms is distinct from that of traditional AI. Our work offers actionable insights to policymakers, developers, and end users. In particular, we call for the prioritization of non-technical risk and harm mitigation strategies that center responsible use, including public disclosures, AI literacy efforts, and careful regulatory stances.