# Implications for Governance in Public Perceptions of Societal-scale AI Risks

arXiv: https://arxiv.org/abs/2406.06199 · HTML: https://mlsanity.com/paper/2406.06199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [The Transformative Potential of Artificial Intelligence](https://mlsanity.com/paper/1912.00747/) (2019)
- [U.S. Public Opinion on the Governance of Artificial Intelligence](https://mlsanity.com/paper/1912.12835/) (2019)
- [Examining the Differential Risk from High-level Artificial Intelligence and the Question of Control](https://mlsanity.com/paper/2211.03157/) (2022)

## Abstract (arXiv)

Amid growing concerns over AI's societal risks--ranging from civilizational collapse to misinformation and systemic bias--this study explores the perceptions of AI experts and the general US registered voters on the likelihood and impact of 18 specific AI risks, alongside their policy preferences for managing these risks. While both groups favor international oversight over national or corporate governance, our survey reveals a discrepancy: voters perceive AI risks as both more likely and more impactful than experts, and also advocate for slower AI development. Specifically, our findings indicate that policy interventions may best assuage collective concerns if they attempt to more carefully balance mitigation efforts across all classes of societal-scale risks, effectively nullifying the near-vs-long-term debate over AI risks. More broadly, our results will serve not only to enable more substantive policy discussions for preventing and mitigating AI risks, but also to underscore the challenge of consensus building for effective policy implementation.