What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them

2026-04-24

AI Summary

This study addresses the public’s limited understanding of the risks and failure modes associated with generative artificial intelligence (GenAI), particularly the lack of awareness regarding how sociotechnical failures can lead to real-world harms. Bridging this gap, the research pioneers an approach that links public risk perceptions to concrete GenAI failure modes by developing and validating a survey instrument grounded in real-world incident scenarios and an expert-verified failure taxonomy. Deployed among 960 participants in the United States, the instrument assesses both risk perception and attribution of responsibility within everyday usage contexts. The method demonstrates scalability and empirical validity in measuring public awareness of AI-related risks, offering actionable insights for AI literacy initiatives and governance policies tailored to authentic user experiences.

Technology Category

• Humans and AI: User Experience and Usability
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Despite growing concerns about the risks of Generative AI (GenAI), there is limited understanding of public perceptions of these risks and their associated failure modes -- defined as recurring patterns of sociotechnical breakdown across the GenAI lifecycle that contribute to risks of real-world harm. To address this gap, we present a survey instrument, validated with eight subject matter experts and deployed on a sample of 960 U.S.-based participants, to assess awareness and perceptions of GenAI's failure modes, their associated risks, and stakeholder responsibilities to address them. To support realism and content validity, our instrument is structured around scenarios grounded in publicly reported incidents and a taxonomy of GenAI's failure modes. Findings suggest that our instrument is (1) effective for assessing risk awareness and perceptions in a way that is grounded in people's current contexts of use, yet is extensible to new contexts that will inevitably arise; and (2) potentially useful for informing the design of AI literacy tools and interventions. We argue for AI literacy and governance approaches that align with how people encounter and reason about GenAI in everyday life.

Problem

• Generative AI
• risk perception
• failure modes
• public understanding
• AI governance

Innovation

• Generative AI
• failure modes
• risk perception
• survey instrument
• AI literacy