photo_camera Jacy Reese Anthis / Midjourney
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Public Opinion and the Rise of Digital Minds: Perceived Risk, Trust, and Regulation Support
Justin Bullock
Research Fellow
Janet Pauketat
Research Fellow
Jacy Reese Anthis
Co-Founder
May 7, 2025

We are pleased to announce our latest peer-reviewed publication, “Public Opinion and the Rise of Digital Minds: Perceived Risk, Trust, and Regulation Support,” in Public Performance & Management Review.

Abstract

Governance institutions must respond to societal risks, including those posed by generative AI. This study empirically examines how public trust in institutions and AI technologies, along with perceived risks, shape preferences for AI regulation. Using the nationally representative 2023 Artificial Intelligence, Morality, and Sentience (AIMS) survey, we assess trust in government, AI companies, and AI technologies, as well as public support for regulatory measures such as slowing AI development or outright bans on advanced AI. Our findings reveal broad public support for AI regulation, with risk perception playing a significant role in shaping policy preferences. Individuals with higher trust in government favor regulation, while those with greater trust in AI companies and AI technologies are less inclined to support restrictions. Trust in government and perceived risks significantly predict preferences for both soft (e.g., slowing development) and strong (e.g., banning AI systems) regulatory interventions. These results highlight the importance of public opinion in AI governance. As AI capabilities advance, effective regulation will require balancing public concerns about risks with trust in institutions. This study provides a foundational empirical baseline for policymakers navigating AI governance and underscores the need for further research into public trust, risk perception, and regulatory strategies in the evolving AI landscape.

The paper is available open access on the journal website: https://www.tandfonline.com/doi/full/10.1080/15309576.2025.2495094

A preprint version is available on ArXiv: https://arxiv.org/abs/2504.21849


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