Graduation Term

Summer 2025

Degree Name

Master of Science (MS)

Department

Department of Politics and Government: Political Science

Committee Chair

Lori Riverstone

Committee Member

LJ Zigerell

Committee Member

Roy D. Magnuson

Abstract

This paper examines the impact of partisanship on public trust in the utilization of Artificial Intelligence (AI) by private and public institutions. This study utilizes data from the “Artificial Intelligence: American Attitudes and Trends” survey (Zhang & Dafoe, 2019). Survey-weighted OLS regression models are employed to examine the variations in trust levels across different private and public institutions. This study hypothesizes that H1: Compared to Republicans, Democrats will report higher levels of confidence in public institutions to develop AI in the best interests of the public. H2: Compared to Democrats, Republicans will report higher levels of confidence in private institutions to develop AI in the best interests of the public. In contrast with the first hypothesis, results reveal that Democrats show lower trust in the military’s use of AI than Republicans do. For all other institutions, Democrats express higher trust in both federal use of AI and private tech companies. The thesis concludes that partisan trust gaps are institution- and context-specific and suggests enhancing AI governance through trust-building.

Access Type

Thesis-Open Access

DOI

https://doi.org/10.30707/ETD.1763755359.149633

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