Data-Centric Governance

• URL: http://arxiv.org/abs/2302.07872v1
• Date: Tue, 14 Feb 2023 07:22:32 GMT
• Title: Data-Centric Governance
• Authors: Sean McGregor and Jesse Hostetler
• Abstract summary: Current AI governance approaches consist mainly of manual review and documentation processes. Modern AI systems are data-centric: they act on data, produce data, and are built through data engineering. This work explores the systematization of governance requirements via datasets and algorithmic evaluations.
• Score: 6.85316573653194
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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