Setting the Agenda for AI: Actors, Issues, and Influence in United States Artificial Intelligence Policy

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Schiff, Daniel S.
Rogers, Juan D.
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As research and adoption of artificial intelligence (AI) has significantly advanced in the early 21st century, determining how to govern AI has become a global priority. Key questions include how AI should be understood as a policy domain, which policy problems are most pressing, which solutions are most viable, and who should have a say in this process. This dissertation seeks to provide key insights into the early years of AI policy, focusing on the development of the emerging AI policy agenda in the United States. To do so, it examines and reveals which issues, actors, and influence efforts are playing a prominent role in the complex, ambiguous, and contested process of agenda-setting. The research performed draws on a variety of quantitative and qualitative methodologies, including document analysis, text-as-data and time series approaches, and experimental techniques. Data examined include text from U.S. federal AI policy documents, traditional and social media discourse from federal policymakers, media, and members of the public, and engagement data collected from state legislators who participated in a field experiment. The results reveal that social and ethical dimensions of AI receive a heightened degree of attention in AI policy discourse. However, consideration of these issues remains partially superficial and subsumed into concern about AI's potential for economic innovation and role in geopolitical competition. Further findings demonstrate that policy entrepreneurs can use persuasive narratives to influence legislators about AI policy, and that these narratives are just as effective as technical information. Finally, despite pervasive calls for public participation in AI governance, the public does not appear to play a key role in directing attention to AI's social and ethical implications nor in shaping concrete policy solutions, such that the emerging AI agenda remains primarily expert-driven. The dissertation's findings and theoretical and methodological approaches offer key contributions to policy process scholarship and related fields of research, and provide a baseline on which to understand the evolution of the AI policy agenda and AI governance going forward.
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