Lessons for Editors of AI Incidents from the AI Incident Database
This work addresses the problem of effectively monitoring and analyzing AI incidents for industry, civil society, and governments, but it is incremental as it builds on existing database efforts.
The study analyzed over 750 AI incidents from the AI Incident Database to identify challenges in indexing and analyzing them, finding that structural ambiguities and epistemic uncertainty are unavoidable, and proposed mitigations to improve incident reporting practices.
As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.