当AI失效时，什么有效？基于数据的现实世界AI风险缓解策略分类
When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies
AI总结：
本文基于数据提出现实世界AI风险缓解策略的分类体系，通过分析9705篇媒体报道的AI事件，提取并分类了6893篇文本中的缓解措施，引入四个新类别，提升分类体系的适用性与实用性。
摘要：
大型语言模型（LLMs）越来越多地嵌入到高风险的工作流程中，其中失败会从孤立的模型错误传播到系统性崩溃，可能导致法律风险、声誉损害和实质性经济损失。在这一从模型为中心的风险向端到端系统漏洞转变的背景下，我们分析现实中的AI事件报告和缓解措施，以推导一个基于实证的分类体系，将失败动态与可操作的干预措施联系起来。使用一个统一的9705篇媒体报道的AI事件文章语料库，我们通过结构化提示从6893篇文本中提取显式缓解措施，然后系统地分类响应以扩展MIT的AI风险缓解分类体系。我们的分类引入了四个新的缓解类别，包括1）纠正和限制措施，2）法律/监管和执法措施，3）财务、经济和市场控制，4）回避和否认，捕捉到随着AI部署和监管发展而日益普遍的响应模式。定量而言，我们对缓解数据集进行了32种不同标签的标注，产生23994个标签分配；其中9629个反映了此前未见的缓解模式，使原有子类覆盖范围增加了67%，显著增强了该分类体系对新兴系统性故障模式的应用性。通过结构化事件响应，本文加强了“诊断到处方”指导，并推进了持续的、与分类体系一致的部署后监控，以防止连锁事件和后续影响。
英文摘要：
Large language models (LLMs) are increasingly embedded in high-stakes workflows, where failures propagate beyond isolated model errors into systemic breakdowns that can lead to legal exposure, reputational damage, and material financial losses. Building on this shift from model-centric risks to end-to-end system vulnerabilities, we analyze real-world AI incident reporting and mitigation actions to derive an empirically grounded taxonomy that links failure dynamics to actionable interventions. Using a unified corpus of 9,705 media-reported AI incident articles, we extract explicit mitigation actions from 6,893 texts via structured prompting and then systematically classify responses to extend MIT's AI Risk Mitigation Taxonomy. Our taxonomy introduces four new mitigation categories, including 1) Corrective and Restrictive Actions, 2) Legal/Regulatory and Enforcement Actions, 3) Financial, Economic, and Market Controls, and 4) Avoidance and Denial, capturing response patterns that are becoming increasingly prevalent as AI deployment and regulation evolve. Quantitatively, we label the mitigation dataset with 32 distinct labels, producing 23,994 label assignments; 9,629 of these reflect previously unseen mitigation patterns, yielding a 67% increase of the original subcategory coverage and substantially enhancing the taxonomy's applicability to emerging systemic failure modes. By structuring incident responses, the paper strengthens "diagnosis-to-prescription" guidance and advances continuous, taxonomy-aligned post-deployment monitoring to prevent cascading incidents and downstream impact.
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。
