Authority Check: A Diagnostic Framework for Judgment, Delegation, and Approval in AI-Assisted Generation

This paper proposes Authority Check (AC), a diagnostic framework for examining judgment, delegation, and approval processes in AI-assisted generation. Unlike approaches that evaluate only final outputs, Authority Check focuses on how judgments are formed, delegated, supplemented by AI, approved by humans, and connected to subsequent improvement cycles. The paper organizes the scope of Authority Check into the final artifact, generation process, and improvement process, and proposes a protocol beginning with Problem Definition, SD structure fixation, LCQ examination, judgment tracing, delegation analysis, approval management, recovery point identification, and next-Plan revision. It also identifies representative Failure Patterns such as Problem Definition Failure, AI-Supplemented Judgment Drift, Delegation and Layer Crossing Failure, Approval Failure, and Recovery Failure. Authority Check is positioned not as a system for replacing human expertise, but as a framework for preserving human judgment authority while using AI-assisted generation systems.