论文标题
通过编目事件来防止重复现实世界的AI失败：AI事件数据库
Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database
论文作者
论文摘要
成熟的工业领域（例如，航空）在事件数据库中收集了现实世界的失败，以指导安全改进。智能系统目前会造成现实世界的伤害，而无需集体记忆失败。结果，公司在智能系统的设计，开发和部署中反复犯同样的错误。需要在现实世界中经历的智能系统失败（即事件）的集合，以确保智能系统使人们和社会受益。 AI事件数据库是由工业/非营利性合作社发起的事件收集，以实现AI事件的回避和缓解。该数据库支持各种研究和开发用例，并在迄今为止已存档的1,000多个事件报告中进行了各个方面和全文搜索。
Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to inform safety improvements. Intelligent systems currently cause real world harms without a collective memory of their failings. As a result, companies repeatedly make the same mistakes in the design, development, and deployment of intelligent systems. A collection of intelligent system failures experienced in the real world (i.e., incidents) is needed to ensure intelligent systems benefit people and society. The AI Incident Database is an incident collection initiated by an industrial/non-profit cooperative to enable AI incident avoidance and mitigation. The database supports a variety of research and development use cases with faceted and full text search on more than 1,000 incident reports archived to date.
