When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption

2026-01-29

AI Summary

This study addresses the severe information asymmetry in the AI consumer market, which impedes users’ ability to identify low-quality systems, suppresses adoption, and undermines market efficiency. Through a simulated market experiment, the research systematically manipulates both the prevalence of low-quality AI systems and the depth of information disclosure, integrating behavioral experiments with Bayesian decision modeling to examine how information asymmetry affects user adoption decisions. The findings provide the first experimental evidence that moderate partial disclosure of system quality effectively mitigates the “lemons problem” in AI markets, significantly improving user decision quality and overall market efficiency. These results offer both theoretical grounding and practical guidance for designing effective information disclosure mechanisms in AI product markets.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

AI consumer markets are characterized by severe buyer-supplier market asymmetries. Complex AI systems can appear highly accurate while making costly errors or embedding hidden defects. While there have been regulatory efforts surrounding different forms of disclosure, large information gaps remain. This paper provides the first experimental evidence on the important role of information asymmetries and disclosure designs in shaping user adoption of AI systems. We systematically vary the density of low-quality AI systems and the depth of disclosure requirements in a simulated AI product market to gauge how people react to the risk of accidentally relying on a low-quality AI system. Then, we compare participants'choices to a rational Bayesian model, analyzing the degree to which partial information disclosure can improve AI adoption. Our results underscore the deleterious effects of information asymmetries on AI adoption, but also highlight the potential of partial disclosure designs to improve the overall efficiency of human decision-making.

Problem

• information asymmetry
• AI adoption
• market for lemons
• disclosure
• AI consumer markets

Innovation

• information asymmetry
• AI adoption
• disclosure design
• experimental market
• Bayesian decision-making