Governing Generative AI in U.S. Higher Education: An Equity-First, Risk-Calibrated Policy Playbook
TL;DR
An equity-first, risk-calibrated policy playbook for higher education leaders is offered by integrating equity-centered leadership perspectives with the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework.
Abstract
Across U.S. higher education, adoption of generative artificial intelligence (GenAI) is expanding faster than many campus governance systems can guide, monitor, or audit. This paper offers an equity-first, risk-calibrated policy playbook for higher education leaders by integrating equity-centered leadership perspectives with the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework. It specifies governance mechanisms, including risk-tiered use-case portfolios, clear decision rights, procurement and vendor-assurance standards, and auditable documentation such as impact assessments. These mechanisms are anchored in U.S. accountability concerns, including privacy, accessibility, nondiscrimination, and due process. Drawing on a policy-design analysis of federal guidance, institutional policy syntheses, and illustrative state/system levers, the paper identifies recurring governance failure modes, including shadow AI use and decision-support tools that become determinative in practice. It specifies proportionate controls campuses can implement without building a parallel bureaucracy. It concludes with a concise measurement approach for internal improvement and public-facing accountability.