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

arXiv: https://arxiv.org/abs/2504.21849 · HTML: https://mlsanity.com/paper/2504.21849/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 100 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey](https://mlsanity.com/paper/2407.08867/) (2024)
- [The AI Double Standard: Humans Judge All AIs for the Actions of One](https://mlsanity.com/paper/2412.06040/) (2024)

## Built on by (most cited first, 3 of 3)

- [Mental Models of Autonomy and Sentience Shape Reactions to AI](https://mlsanity.com/paper/2512.09085/) (2025)
- [The Sentience Readiness Index: A Preliminary Framework for Measuring National Preparedness for the Possibility of Artificial Sentience](https://mlsanity.com/paper/2603.01508/) (2026)
- [Method, Mind, and Morality: How People Make Sense of Artificial Intelligence](https://mlsanity.com/paper/2608.24748/) (2026)

## Abstract (arXiv)

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.