A data-governance paper asks how organizations can use AI-generated metadata without reducing human review to an unexplained approval. Industrial knowledge often sits across both structured records and documents. Classifying and linking those sources requires domain context, and an incorrect metadata proposal can spread into downstream systems.
Make disputed decisions inspectable
The paper proposes contestable governance. Specialist agents produce scored arguments with citations for disputed metadata decisions. A human steward can accept, reject, modify or add arguments before the decision is committed.
The governance record retains the evidence, interventions and outcome. That creates a more specific review trail than an approval flag alone: another reviewer can see what was disputed and how the steward changed or accepted the reasoning.
Use domain context without making every case expensive
A versioned glossary supplies model-specific terms and acronym definitions. The workflow also includes triage that reserves full argumentation for uncertain proposals while preserving review before commit.
The authors describe a gate controlling which verified decisions become precedent. That boundary matters because a single mistaken classification can otherwise influence later automated suggestions. Reusing a decision should depend on the evidence and review state attached to it, rather than only its existence in the history.
Illustrations establish the pattern, not a production score
Two HVAC examples illustrate the proposal. One concerns point classification with Brick Schema labels and an open reference implementation. The other is an illustrative warranty failure-mode decision connecting claim fields and telemetry with service notes and revised bulletins.
These examples explain the workflow; they do not provide a broad measured reduction in governance errors. For teams evaluating similar systems, the useful question is whether the record lets a later steward reconstruct the basis of a decision and challenge it. The paper contributes a review and record model, with broader operational effectiveness requiring evaluation in the intended organization.
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