深入审视生成式AI的现有风险：映射现实事件中的谁、什么和如何
A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents
AI总结：
本文通过分析499个公开事件，揭示生成式AI失败导致的伤害类型、成因及影响方，发现多数事件由使用问题引发，但伤害范围超出了终端用户，且生成式AI的风险图景不同于传统AI。
摘要：
由于其通用性，生成式AI被应用于越来越多的领域和任务，导致对人、社区、社会和环境造成伤害的风险不断扩大。这些风险可能源于技术设计和开发过程中的失败，以及其发布、部署或下游使用中的问题。本文基于先前的AI风险、伤害和失败分类，构建了专门针对生成式AI失败的分类，并将其映射到其引发的伤害。通过系统分析499个公开报告的事件，我们描述了报告的伤害类型、其成因及影响方。我们报告了每种伤害的普遍性、根本性故障模式及受影响的利害相关者及其常见共现情况。我们发现，大多数报告的事件是由使用相关问题引起的，但导致的伤害影响超出了生成式AI系统故障的终端用户。我们发现生成式AI的伤害图景不同于传统AI。本文为政策制定者、开发者和生成式AI用户提供了可操作的见解。特别是，我们呼吁优先考虑非技术性风险和伤害缓解策略，包括公开披露、教育和谨慎的监管立场。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
