Data-Centric Governance. (arXiv:2302.07872v1 [cs.CY])

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.