This area examines AI governance through the lens of political science and public policy — how new technology issues enter the legislative agenda, how they become polarized along partisan lines, and what institutional arrangements are needed to govern AI at national and international scales.
Empirical work uses agenda-setting theory and comparative policy analysis to trace how AI has moved from a technical concern to a contested political issue. This includes original data collection on legislative attention, cross-national regulatory comparisons, and studies of how different political systems frame and delegate AI governance authority. One strand examines the partisan dynamics driving AI polarization in U.S. politics; another maps the global regulatory landscape using the AGORA archive developed with Georgetown CSET.
A normative thread runs alongside the empirical: what principles should anchor AI governance, and how do we move from abstract values to institutional design? This includes work on an AI social contract — the mutual obligations between AI developers, governments, and the public — and on the governance infrastructure needed to make international coordination legible and accountable.