Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

TL;DR

This study compares perceptions of AI risks, benefits, and value between the public and experts across various scenarios, revealing significant perception gaps that impact AI governance and societal trust.

Contribution

It provides empirical insights into the perceptual differences between experts and the public on AI, highlighting areas for improved communication and policy intervention.

Findings

Experts perceive lower risks and higher benefits than the public.

Both groups evaluate AI in medical diagnosis as highly convergent.

Tension points include AI in legal decisions and political contexts.

Abstract

Artificial Intelligence (AI) is reshaping many societal domains, raising critical questions about its risks, benefits, and the potential misalignment between public and academic perspectives. This study examines how the general public (N=1110) -- individuals who interact with or are impacted by AI technologies -- and academic AI experts (N=119) -- those elites shaping AI development -- perceive AI's capabilities and impact across 71 scenarios. These scenarios span domains such as sustainability, healthcare, job performance, societal inequality, art, and warfare. Participants evaluated these scenarios across four dimensions using the psychometric model: likelihood, perceived risk and benefit, and overall value (or sentiment). The results suggest significant differences: experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts. Moreover, both groups apply different weighting schemes: experts discount risk more heavily relative to benefit than non-experts. Visual mappings of these evaluations uncover areas convergent evaluations (e.g., AI performing medical diagnoses or criminal use) as well as tension points (e.g., decision of legal cases, political decision making), highlighting areas where communication and policy interventions may be needed. These findings underscore a critical translational challenge: if AI research and deployment are to align with societal priorities, the perception gap between developers and the public must be better understood and addressed. Our results provide an empirical foundation for value-sensitive AI governance and trust-building strategies across stakeholder groups.