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
Alexander Erlei, Federico Maria Cau, Radoslav Georgiev, Sagar Chethan Kumar, Kilian Bizer, Ujwal Gadiraju
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext adc3c603-3d69-4930-9e53-d595db4e7ef4Cited by top-tier papers2
- Belief Updating and Delegation in Multi-Task Human-AI Interaction: Evidence from Controlled SimulationsShreyan Biswas, Alexander Erlei, Ujwal GadirajuCHI 2026 · 4 citations
- The Data-Dollars Tradeoff: Privacy Harms vs. Economic Risk in Personalized AI AdoptionAlexander Erlei, Tahir Abbas, Kilian Bizer, Ujwal GadirajuCHI 2026 · 1 citation
Builds on30
- What is AI Literacy? Competencies and Design ConsiderationsDuri Long, Brian MagerkoCHI 2020 · 2,947 citations
- Manipulating and Measuring Model InterpretabilityForough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan et al.CHI 2021 · 663 citations
- Explanations Can Reduce Overreliance on AI Systems During Decision-MakingHelena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg et al.CSCW 2023 · 362 citations
- Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-makingCharvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney et al.CSCW 2022 · 184 citations
- Conceptual Metaphors Impact Perceptions of Human-AI CollaborationPranav Khadpe, Ranjay Krishna, Li Fei-Fei, Jeffrey T. Hancock et al.CSCW 2020 · 179 citations
Related papers
- Who is Responsible, the Advisor or the AI? Understanding the Effects of Advisors Disclosing Their AI Use on Their Perceived Responsibility and AI RelianceTamir Mendel, Soumik Mandal, Oded Nov, Batia Mishan WiesenfeldCSCW 2025 · 6 citations
- "Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance WorkAngel Hsing-Chi Hwang, Senya Wong, Baixiao Chen, Jessica He et al.CHI 2026 · 2 citations
- Robust Human-AI Complementarity under UncertaintyYewon Byun, Bryan WilderICML 2026
- AI Knowledge: Improving AI Delegation through Human EnablementMarc Pinski, Martin Adam, Alexander BenlianCHI 2023 · 58 citations
- The Effects of Warmth and Competence Perceptions on Users' Choice of an AI SystemZohar Gilad, Ofra Amir, Liat LevontinCHI 2021 · 54 citations
