为关键数字基础设施的AI事故建立标准化记录框架，提升安全与透明度。
Standardised schema and taxonomy for AI incident databases in critical digital infrastructure
- 设计统一的数据结构和分类体系，规范AI事故记录
- 新增事故严重性、原因、造成的损害等字段，增强数据颗粒度
- 适合政策制定者、行业安全团队及监管机构参考使用
人工智能在关键数字基础设施中的快速部署带来了重大风险，亟需系统化收集AI事故数据以预防未来事件。现有数据库缺乏足够的细节和标准化结构，难以实现一致的数据采集与分析。本文提出一套用于AI事故数据库的标准架构与分类体系，通过统一数据格式，引入事故严重性、原因、造成的危害等新字段，支持跨行业的精细化事故记录。该方案有助于更有效地收集与分析事故数据，推动基于证据的政策制定，强化行业安全措施，促进透明治理。本工作为全球协同应对AI事故奠定了基础，确保各地区在使用AI时具备信任、安全与问责机制。
原文摘要 · Abstract (English)
The rapid deployment of Artificial Intelligence (AI) in critical digital infrastructure introduces significant risks, necessitating a robust framework for systematically collecting AI incident data to prevent future incidents. Existing databases lack the granularity as well as the standardized structure required for consistent data collection and analysis, impeding effective incident management. This work proposes a standardized schema and taxonomy for AI incident databases, addressing these challenges by enabling detailed and structured documentation of AI incidents across sectors. Key contributions include developing a unified schema, introducing new fields such as incident severity, causes, and harms caused, and proposing a taxonomy for classifying AI incidents in critical digital infrastructure. The proposed solution facilitates more effective incident data collection and analysis, thus supporting evidence-based policymaking, enhancing industry safety measures, and promoting transparency. This work lays the foundation for a coordinated global response to AI incidents, ensuring trust, safety, and accountability in using AI across regions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。