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

TL;DR

This paper investigates how information asymmetries and disclosure strategies influence consumer adoption of AI systems, revealing that partial disclosures can mitigate risks and improve market efficiency.

Contribution

It provides the first experimental analysis of how disclosure design impacts AI adoption amid information asymmetries in a simulated market.

Findings

Information asymmetries hinder AI adoption.

Partial disclosures can improve decision-making.

Market efficiency benefits from better disclosure strategies.

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.