Indexing AI Risks with Incidents, Issues, and Variants
This work addresses the need for better risk management in AI systems, though it is incremental as it builds on existing database practices from aviation and computer security.
The paper tackles the problem of categorizing AI-related harms and risks by proposing a two-tiered indexing system for incidents and issues, based on lessons from editing over 2,000 reports.
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."