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While certain industrial sectors (e.g., aviation) have a long history ofmandatory incident reporting complete with analytical findings, the practice ofartificial intelligence (AI) safety benefits from no such mandate and thusanalyses must be performed on publicly known ``open source'' AI incidents.Although the exact causes of AI incidents are seldom known by outsiders, thiswork demonstrates how to apply expert knowledge on the population of incidentsin the AI Incident Database (AIID) to infer the potential and likely technicalcausative factors that contribute to reported failures and harms. We presentearly work on a taxonomic system that covers a cascade of interrelated incidentfactors, from system goals (nearly always known) to methods / technologies(knowable in many cases) and technical failure causes (subject to expertanalysis) of the implicated systems. We pair this ontology structure with acomprehensive classification workflow that leverages expert knowledge andcommunity feedback, resulting in taxonomic annotations grounded by incidentdata and human expertise.

A taxonomic system for failure cause analysis of open source AI incidents