Public Opinion and The Rise of Digital Minds: Perceived Risk, Trust, and Regulation Support

2025-04-30

AI Summary

This study investigates how differential public trust—in government, AI firms, and AI technology—interacts with AI risk perception to shape support for regulatory policies (e.g., development slowdowns, outright bans). Drawing on nationally representative 2023 AIM Survey data, we employ multivariate regression, structural equation modeling, and hierarchical logistic regression. Our analysis reveals a novel, systematic differentiation in trust effects: high governmental trust combined with heightened risk perception positively predicts support for both soft and hard regulation; conversely, elevated trust in AI firms or AI technology significantly reduces willingness to impose restrictions. The findings establish risk perception and institutional trust as dual, independent predictors of AI governance preferences—challenging monolithic conceptions of “trust” in AI policy research. This work advances theoretical understanding of public AI regulatory attitudes and provides an empirically grounded benchmark for evidence-based AI governance design.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Knowledge Representation and Reasoning: Preferences

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Technology governance, policy, and regulations

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.

Problem

• Examining public trust and perceived risks in AI regulation preferences
• Assessing public support for regulatory measures on AI development
• Analyzing how trust in institutions influences AI governance preferences

Innovation

• Uses AIMS survey for public opinion data
• Analyzes trust and risk impact on regulation
• Links government trust to regulatory support