Contestable Agents for Data Governance: Recording Human Stewardship of AI-Generated Metadata Across Structured and Unstructured Systems· 数据治理中的可争议代理
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 record of what a reviewer checked. We present contestable governance: specialist agents produce scored, cited arguments for disputed metadata decisions; a steward can accept, reject, modify, or add argume
提出可争议治理模型，解决 AI 生成元数据的审核痕迹记录问题。
- 核心方法
- 通过构建基于大型语言模型的代理，为元数据决策提供可评分、可引用的论据，确保每个决策都有明确的人类审核记录。
- 适合谁读
- 研究者、工程师、产品、数据治理人员
- 要解决的问题
- AI 生成的元数据未经充分审核就被采纳，可能导致错误传播，缺乏审核记录。
- 关键实验
- 未提供
- 主要贡献
- 引入了可争议治理的概念，提高了元数据决策的透明度和可追溯性。
- 意义与局限
- 增强了数据治理的可靠性和效率，但可能需要更多的人力资源来培训审核人员。