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Kennedy Shorter

Kennedy Shorter

MIT Department: Political Science
Faculty Mentor: Prof. Ariel White
Undergraduate Institution: North Carolina A&T University
Website:

Biography

Kennedy Shorter is a senior honors student studying Political Science at North Carolina A&T State University. Originally from Houston, Texas, she is passionate about environmental justice, civil rights, and the ethical implications of emerging technologies. This summer, she is researching political ideologies and AI regulation through the MIT Summer Research Program, exploring how governance structures can shape the future of artificial intelligence. Kennedy’s experiences, from student government leadership to her work with NSF-funded research on programmable plants, have deepened her interest in the intersection of technology and policy.After graduation, she plans to pursue a dual degree program combining a Juris Doctor and aMaster’s degree, aiming to become an attorney specializing in technology or business law.With a strong interdisciplinary foundation and a commitment to justice, equity, and responsible innovation, Kennedy strives to be a leader in shaping ethical and inclusive policies for a rapidly evolving world.

Abstract

Public Opinion and AI Legislation in the U.S. (2020-2025) Are Lawmakers Listening?

Kennedy Shorter1

1Department of History and Political Science, North Carolina A&T State University

As artificial intelligence (AI) rapidly evolves, federal legislation in the United States struggles to keep pace. This study examines whether Congress has meaningfully responded to public concern over AI, particularly regarding regulation. Using survey data from Pew, YouGov, and Gallup (2020–2025), I analyze trends in public demand for increased regulation of AI in sectors like education, employment, and healthcare. Despite consistent majority support for stronger oversight, my findings show that congressional action remains limited and often favors the interests of corporations over constituents. I compare shifts in public opinion to the timing and content of federal AI-related bills, using text analysis to detect references to constituent harms or regulatory goals. Drawing on frameworks from Gilens & Page (2014) and Burstein (2003), I assess legislative responsiveness within a one-year window of public concern. Early results suggest a policy disconnect: while public support for regulation rises, Congress prioritizes innovation and economic interests. This has profound implications for democratic representation in emerging tech governance. If policy continues to follow elite interest rather than popular demand, the U.S. risks a civil oligarchy instead of meaningful public accountability.
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