论文标题
用事件，问题和变体索引AI风险
Indexing AI Risks with Incidents, Issues, and Variants
论文作者
论文摘要
在公开启动AI事件数据库（AIID）的两年后，作为世界上AI造成的危害或几乎危害的收集，“问题”的积压不符合其事件摄入标准的审查队列。尽管没有通过数据库目前的事件标准，但这些问题使人类对AI在何处造成的伤害的可能性提高了人们的理解。与航空和计算机安全性数据库类似，AIID建议采用两层系统来索引AI事件（即危害或几乎危害事件）和问题（即发生伤害事件的风险）。此外，由于某些基于机器学习的系统有时会产生大量事件，因此引入了事件的概念。这些提出的更改标志着AIID向新版本的过渡，以回应从编辑2,000多个事件报告中学到的经验教训，以及属于“问题”新类别的其他报告。
Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."
