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."

AI Incident Database 提出了对于 AI 事件（即危害或可能危害的事件）和问题（即危害事件的风险）采用两层索引系统的方案，并因此引入了 “事件变体” 的概念，以应对大量机器学习产生的事件，该提案是 AIID 根据 2000 多个事件报告和新的 “问题” 类别的教训而过渡到新版本的反应。