A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents

TL;DR

This paper systematically analyzes 499 real-world incidents involving Generative AI, mapping risks and harms to inform better mitigation strategies and policy decisions.

Contribution

It constructs a taxonomy of Generative AI failures, analyzes incident reports, and highlights the need for non-technical risk mitigation approaches.

Findings

Most incidents are caused by use-related issues.

Harms often affect parties beyond end users.

Generative AI harms differ from traditional AI harms.

Abstract

Due to its general-purpose nature, 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 precipitate. Through a systematic analysis of 499 publicly reported incidents, we describe what harms are reported, how they arose, and who they impact. We report the prevalence of each type of harm, underlying failure mode, and harmed stakeholder, as well as their common co-occurrences. We find that most reported incidents are caused by use-related issues but bring harm to parties beyond the end user(s) of the Generative AI 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 Generative AI users. In particular, we call for the prioritization of non-technical risk and harm mitigation strategies, including public disclosures and education and careful regulatory stances.