When Human Review Is Treated as Ground Truth: A Socio-Technical Analysis of Reference Dependence and Measurement Validity in Human-in-the-Loop AI Inspection Systems

Human-in-the-loop pipelines are widely used in artificial intelligence (AI)-based inspection systems, coupling an automated model, human reviewers, and an organizational reporting layer that drives decisions and, potentially, model retraining. The closed loop this creates is a design feature, not a defect; the pathology lies one level up, where a record produced by editing the model’s own output is treated as an independent reference and the automated-versus-reviewed difference is reported as “accuracy”—a closed loop read as open. This paper offers no alternative estimator; it shows that none is available here and that the infeasibility is itself the diagnosis. We examine the loop in a national pavement-management program, matching automated and reviewed outputs for 349 images across 162,520 individual grids. Reviewers accepted 89.06% of grids without change, added 10.25%, removed 0.28%, and reclassified 0.42% (an addition-to-removal ratio of 36.4:1); patching accounted for 64.5% of the added grids (39.8–69.0% across leave-one-route-out folds), added grids largely recording maintenance patches, not cracks. Consistent with the documented review workflow, 24 images were identical even by grid type and 98.06% of automated distress grids survived review (97.6–99.6% across folds); the reviewed record therefore cannot serve as an independent reference, and detection accuracy cannot be estimated. Across 40 route segments, the dependence mechanism and the asymmetry’s direction are invariant; the composition and magnitude are not. We reinterpret the reported figure and derive systems-level interventions—reporting items, interface priorities, and independent-reference requirements.