AI Governance and Regulation: From State to Federal

Session Submission Type: Panel

Abstract

The panel will foster a discussion and dialogue for ongoing research on state and federal AI governance and policy research and the practical application of open datasets to federal AI policy and standards. We have included a variety of panelist perspectives from the policy community. The rapid integration of artificial intelligence (AI) into public and private sectors has outpaced federal regulatory frameworks in the U.S., leaving states to act as de facto laboratories for AI governance. States have increasingly introduced and passed AI regulation primarily focusing on data privacy, accountability, and ethical deployment. However, the lack of federal coordination risks creating a fragmented regulatory landscape, complicating compliance for multistate actors and undermining equitable protections. State laws represent public demands for consumer protection from AI harms and potential challenges for AI companies in terms of navigating patchwork compliance burdens. Laws relating to practices like safety standards, data safeguards, and other issues could have national implications, particularly if they result in court cases that set precedent. However, in the current absence of such cases, the implications of these laws for U.S. competitiveness and national security is difficult to assess. Our panel would address two core questions:  What distinctions exist in regulatory approaches across states, and how do these reflect divergent priorities (e.g., innovation vs. consumer protection or interests tied to state-specific industries), scopes (e.g., sectoral coverage), regulatory targets (e.g., data, compute, model weights) and capacity building efforts (e.g., strengthening AI-enabling talent and building evaluation infrastructure)?  Which state-level initiatives offer scalable best practices or approaches for balancing AI’s economic potential with reliable safeguards against bias, privacy violations, and transparency gaps?  This research aligns with broader goals of identifying policy models that reconcile AI advancement with governance best practices in democratic societies. By analyzing different legislative approaches and challenges, the effort will inform cohesive state, multistate, or federal AI governance strategies.

Policy Area

• Innovations in Science and Technology

Chair

• Shruti Agarwal, Georgetown University

Discussant

• Kelly Beavan, Georgetown University

Organizer

• Veronica Jade Kinoshita, Georgetown University

Individual Presentations

• From Symbolic Attention to Operational Rules: Explaining Variation in State AI Legislation - Presenting Author: Ruoxi Li, The Hong Kong University of Science and Technology (HKUST)

• Strategizing AI Policy in State Governments: Balancing Innovation and Governance - Presenting Author: Pedro Robles, Pennsylvania State University

• Fifty States, Fifty Algorithms: State-Level Variation in AI Governance and Its Implications for Public Trust - Presenting Author: Sonia Junior Arthur, University of Massachusetts at Boston