Governing Artificial Intelligence: Lessons from the United States and China

This dissertation analyzes how artificial intelligence (AI) technologies are being governed, with an emphasis on the experiences of the United States and China. The thesis is positioned at the intersection of platform- and technology-related governance and regulation, rooted in literature emanating from disciplines such as information systems, institutional theory, and political economy. The thesis elaborates on a range of governance mechanisms for AI located across technical, organizational, and institutional levels. Drawing on the empirical cases of the United States and China, the overarching research question of the thesis inquires: how is artificial intelligence governed in the United States and China, and what are some of the broader implications for the governance of AI?
The motivation for this research question rests on the insight that the approaches to AI governance by the United States and China will inform AI governance regimes elsewhere in significant ways. While scholars from several academic fields have contributed to the existing literature on AI governance, many unfulfilled gaps have barely been dealt with. In particular, as more vigorous calls for AI regulation have emerged, little is known about the interactions between new and incoming AI regulation and firm-level behavior and innovation. Second, while AI technologies are already implemented in most sectors and industries, little is known about how discrete AI fields gain legitimacy and become institutionalized over time. Third, while a great number of national AI policies and innovation strategies have been released, how these interact with and affect AI innovation has been little studied. Finally, even though international competition in areas such as AI and semiconductors is on the rise, the effects of great power competition on technological governance and data privacy preferences have been little studied.