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 ad-vance 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.” In November of 2020, the AIID publicly launched as a collection of harms and near harms realized in the real world involving intelligent systems [4]. Now two years later, the database has expanded to more than 300 incidents characterized by more than 2,000 incident reports. These indexing activ-ities have engendered considerable discussion around incident reporting as a potential mechanism to bring about transparency, accountability, and responsible governance of AI systems [8, 6, 2]. As the AIID organizes various policy, corporate, and researcher stakeholder groups to collect and share AI incidents, concept definitions play an imperative role in setting standards and common understanding. As AI incident reporting is a fast developing practice, definitions must simultaneously be implementable in future mandatory and voluntary