Charting the AI Perception Gap: Across 71 scenarios, AI experts (N=119) and the public (N=1100) have differing views on the risks, benefits, and value of AI. More importantly, AI experts discount the influence of risks stronger than the public does when forming their value judgments [R]

2026-05-05 · Source: Machine Learning · Field: Science & Research — Social Sciences & Behavioral Studies, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A study involving 119 German AI experts and 1,110 members of the German public reveals significant differences in their perceptions of artificial intelligence across 71 diverse scenarios, including sustainability, healthcare, and warfare. Experts generally anticipate higher probabilities of AI occurrence, perceive lower risks, and report greater benefits compared to the public, leading to more positive overall evaluations. Crucially, experts' value judgments are primarily driven by perceived benefits, while the public's evaluations place more weight on perceived risks. This divergence in mental models suggests that current AI research and implementation may inadvertently overlook public risk-related priorities, potentially leading to "procrustean AI" systems that are not adequately aligned with societal needs. The study identifies both convergent domains, like medical diagnoses, and tension points, such as justice and political decision-making, which may require targeted communication or policy interventions.

Key takeaway

For research scientists developing AI systems, understanding the public's heightened sensitivity to AI risks is critical. Your work may inadvertently neglect public risk priorities if driven solely by perceived benefits, leading to systems misaligned with societal needs. Actively seek public input and integrate participatory practices into your development lifecycle to ensure AI systems are robust, trustworthy, and broadly accepted.

Key insights

AI experts and the public hold systematically different mental models regarding AI's risks, benefits, and overall value.

Principles

• Expert AI value judgments prioritize benefits.
• Public AI value judgments prioritize risks.
• Divergent mental models create "procrustean AI" risk.

Method

The study used a psychometric model to evaluate 71 AI scenarios across likelihood, perceived risk, perceived benefit, and overall value, surveying 119 AI experts and 1,110 public individuals in Germany.

In practice

• Target communication for AI tension points.
• Implement participatory AI governance.
• Align AI development with public risk priorities.

Topics

• AI Perception Gap
• Risk-Benefit Perceptions
• Public vs. Expert Views
• AI Value Judgments
• AI Governance

Best for: Research Scientist, AI Scientist, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.