  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."
