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Contestable Agents for Data Governance: Recording Human Stewardship of AI-Generated Metadata Across Structured and Unstructured Systems

Humza Akhtar, Brian Freeman · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31553 · open-access PDF

Portuguese

As organizações industriais possuem conhecimentos operacionais em registros estruturados, como telemetria, listas de pontos e transações, e em fontes não estruturadas, como manuais, notas de serviço, comunicações e relatórios de inspeção. Programas de gerenciamento geralmente gerenciam essas fontes separadamente. Classificar, vincular e documentá-las depende de um pequeno grupo de funcionários com profundo conhecimento de domínio. Agentes construídos em modelos de idiomas grandes podem propor metadados, citar documentos e questões de qualidade de bandeira, mas propostas não revisadas podem espalhar erros em sistemas subsequentes.

Machine translation (Meta M2M100); the English original follows.

Abstract

Industrial organizations hold operational knowledge in structured records, such as telemetry, point lists, and transactions, and in unstructured sources, such as manuals, service notes, communications, and inspection reports. Governance programs often manage these sources separately. Classifying, linking, and documenting them depends on a small group of employees with deep domain knowledge. Agents built on large language models can propose metadata, cite documents, and flag quality issues, but unreviewed proposals can spread errors into downstream systems. A perfunctory approval leaves no reco

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