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.

翻译：人工智能消费市场呈现出严重的买方-供应商市场不对称特征。复杂的AI系统可能在表现出高度准确性的同时产生代价高昂的错误或隐藏缺陷。尽管围绕不同形式的披露已存在监管努力，巨大的信息鸿沟依然存在。本文首次通过实验证据揭示了信息不对称与披露设计在塑造用户对AI系统采用行为中的关键作用。我们在模拟AI产品市场中系统性地改变低质量AI系统的密度与披露要求的深度，以衡量人们对意外依赖低质量AI系统风险的反应。随后，我们将参与者的选择与理性贝叶斯模型进行对比，分析部分信息披露能在多大程度上改善AI采用率。研究结果不仅证实了信息不对称对AI采用的有害影响，同时凸显了部分披露设计在提升人类决策整体效率方面的潜力。