Why Most AI Incidents Are Evidence Failures, Not Model Failures

Public discourse on AI risk continues to frame incidents primarily as technical failures: model bias, hallucination, or misconfiguration. This article advances a different interpretation grounded in governance practice. Drawing on patterns observable in the OECD AI Incidents Monitor, it argues that many AI incidents escalate not because models fail, but because institutions cannot reconstruct what AI systems said, when they said it, and how those representations were framed at the moment of reliance. The article does not assess model accuracy, internal system design, or causality. Instead, it examines AI incidents as post-event accountability failures driven by missing or non-inspectable evidence. Through sector-agnostic walkthroughs spanning finance, healthcare, and public administration, it demonstrates a recurring governance failure mode: once scrutiny occurs, the absence of contemporaneous, interaction-specific records converts uncertainty into institutional exposure regardless of technical intent or system quality. The paper reframes AI incident management as an evidentiary control problem rather than a model optimization problem. It concludes that, in non-deterministic systems deployed as external representation channels, accountability depends less on improving prediction accuracy than on preserving inspectable records of AI-mediated representations at the point of human reliance.