# Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance

arXiv: https://arxiv.org/abs/2411.19356 · HTML: https://mlsanity.com/paper/2411.19356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 109 references that are in the map.
- First posted on arXiv 28 Nov 2024.

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

- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Long-Term Trends in the Public Perception of Artificial Intelligence](https://mlsanity.com/paper/1609.04904/) (2016)
- [Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countries](https://mlsanity.com/paper/2001.00081/) (2019)
- [Mapping acceptance: micro scenarios as a dual-perspective approach for assessing public opinion and individual differences in technology perception](https://mlsanity.com/paper/2402.01551/) (2024)
- [The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success](https://mlsanity.com/paper/2402.14728/) (2024)

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

- [What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework](https://mlsanity.com/paper/2508.19317/) (2025)
- [Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI](https://mlsanity.com/paper/2412.01459/) (2024)
- [Cultural Dimensions of AI Perception: Charting Expectations, Risks, Benefits, Tradeoffs, and Value in Germany and China](https://mlsanity.com/paper/2412.13841/) (2024)

## Abstract (arXiv)

Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 participants from Germany, this study examines mental models of AI. Participants quantitatively evaluated 71 statements about AI's future capabilities (e.g., autonomous driving, medical care, art, politics, warfare, and societal divides), assessing the expected likelihood of occurrence, perceived risks, benefits, and overall value. We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs. While many scenarios were deemed likely, participants often associated them with high risks, limited benefits, and low overall value. Across all scenarios, 96.4% (r2=96.4%) of the variance in value assessment can be explained by perceived risks (β=-.504) and perceived benefits (β=+.710), with no significant relation to expected likelihood. Demographics and personality traits influenced perceptions of risks, benefits, and overall evaluations, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs. These findings provide actionable insights for researchers, developers, and policymakers by highlighting critical public concerns and individual factors essential to align AI development with individual values.