A New Field of AI Incident Investigations

When an AI incident occurs, whether caused by misalignment, misuse, or system failure, the immediate challenge is not only responding to the event, but also understanding what actually happened. What did the system do? When and why did it happen? What evidence can be collected? Who is accountable? And ultimately, what are the lessons that can be learned? Just as importantly, how much of this can realistically be established from the outside? In other high-risk domains, where investigation practices have long been established, there would be a way of answering these questions; for AI, most of the time, there is not yet.

What the field is. AI incident investigation is an emerging practice, still taking shape, of detecting, documenting, classifying and analysing harm events, and near-harms, involving deployed AI systems, so that they are not simply repeated and in order for what is learned to inform how these systems are governed and produce changes which lower their overall risk.

Scope map // the territory, by the AI system's role in the incident

note: prompt injection straddles 2 ↔ 3. The AI is target and instrument at once. Boundary cases are normal; the taxonomy serves the investigation, not the reverse.

Browse the resource // each section now on its own page

• Published Research: library of publications on AI incident investigation.
• Case files: selected incident summaries; observation separated from inference.
• Frameworks: existing methodologies, what each covers and does not.
• Regulatory tracker: reporting obligations and deadlines.
• Tools & databases: the current ecosystem.
• Contribute: case files, corrections, regulatory updates.