Artificial intelligence (AI) governance is the body of standards and practices used to ensure that AI systems are deployed responsibly. Current AI governance approaches consist mainly of manual review and documentation processes. While such reviews are necessary for many systems, they are not sufficient to systematically address all potential harms, as they do not operationalize governance requirements for system engineering, behavior, and outcomes in a way that facilitates rigorous and reproducible evaluation. Modern AI systems are data-centric: they act on data, produce data, and are built through data engineering. The assurance of governance requirements must also be carried out in terms of data. This work explores the systematization of governance requirements via datasets and algorithmic evaluations. When applied throughout the product lifecycle, data-centric governance decreases time to deployment, increases solution quality, decreases deployment risks, and places the system in a continuous state of assured compliance with governance requirements.

翻译：人工智能（AI）治理是确保AI系统负责任部署的标准与实践体系。当前的AI治理方法主要包括人工审查与文档处理流程。尽管此类审查对诸多系统而言不可或缺，但因其未能将治理要求系统性地转化为可操作的系统工程、行为及结果规范，以实现严谨且可复现的评估，故不足以全面解决所有潜在危害。现代AI系统以数据为核心：它们基于数据运行、产生数据，并通过数据工程构建。治理要求的保障也必须通过数据实现。本文探索了通过数据集与算法评估实现治理要求系统化的路径。当该方法贯穿产品生命周期时，以数据为中心的治理能够缩短部署周期、提升解决方案质量、降低部署风险，并使系统持续处于符合治理要求的受保障状态。