A Diagnostic Analysis of a Governance Failure Class in Contemporary AI Deployment

This document presents a diagnostic analysis of a governance failure class observed in contemporary AI deployment. It does not propose a policy framework, technical method, or implementation pathway. Its purpose is to establish the existence, structure, and location of a failure mode that arises prior to identifiable system risk, and to clarify why existing governance approaches do not capture it. The analysis identifies a governance inversion in which trust, delegation, and reliance are extended under ordinary conditions of use before specific risks are recognisable. As a result, the conditions that enable meaningful risk identification are themselves ungoverned. The failure class occurs at the level of human–system coupling and concerns the allocation of interpretive authority, the normalisation of reliance, and the implicit transfer of responsibility prior to observable malfunction, misuse, or harm. This document is diagnostic rather than prescriptive. It establishes a failure class but does not enumerate its manifestations or propose mitigation mechanisms.