A Public Values Framework for AI Governance: Evidence From US Federal AI Policymaking

ABSTRACT Governments at all levels are grappling with AI's implications for public administration. Because federal actions often establish the principles guiding wider governance efforts, this study asks how US federal AI policies reflect—or fail to reflect—core public values. Drawing on a dataset of US federal AI policies from 2020 to 2023, we systematically analyze the risks, harms, and governance strategies employed across government‐issued and enacted policy documents. Specifically, we analyze 120 discrete policy sections or subsections of federal laws and regulations, utilizing the parsing approach of the AI Governance and Regulatory Archive (AGORA), all of which regulate the operation of the government itself rather than private sector behavior. We develop a theoretical and empirical crosswalk linking AI‐related risks and harms to prominent public values. Results indicate that while values such as individual rights, the common good, and accountability are regularly invoked, additional guidance around public values like citizen engagement, workplace standards, and responsiveness may be necessary to prevent public value failure. The study further categorizes governance strategies in AI policy documents into regulatory, service‐based, and informational instruments, and finds that regulatory policies are most likely to engage with public values.