A Judgment-First Incident Review Framework for Irreversible AI Systems

Incidents involving irreversible AI systems often result in accountability failures even when system decisions are technically correct, procedurally compliant, and aligned with stated objectives. Existing incident review practices predominantly evaluate decision quality, model behavior, or regulatory adherence, implicitly assuming that correct decisions imply legitimate execution. This assumption does not hold under irreversible conditions. This paper argues that in systems capable of producing irreversible physical or systemic outcomes, accountability cannot be reconstructed after execution; it must exist structurally before execution occurs. We show that post-hoc explanations derived from opaque or probabilistic models provide only illusory evidence for accountability, as they cannot establish whether execution itself was authorized. To address this structural failure, we introduce the Judgment-First Incident Review Framework (JFIR), which reorients incident analysis around pre-execution authorization rather than post-execution reasoning. JFIR defines a mandatory review order that begins with authorization verification (JFIR-Q0), followed by authority traceability and scope consistency assessment, and only conditionally proceeds to decision quality analysis. Central to this framework is Authority Traceability, a verifiable audit object that binds irreversible execution to an identifiable authorizing entity, authorization state, and timestamp. By formalizing accountability as a function of structural authorization rather than decision correctness, this work establishes the logical preconditions for valid incident review in irreversible AI systems. JFIR does not propose a new governance mechanism, but defines the minimal conditions that any legitimate incident review must satisfy to establish authentic accountability under irreversible conditions.