Till Tech Do Us Part: Betrayal Aversion and Its Role in Algorithm Use

• 成果类型: Article; Early Access
• 署名作者: Kormylo, Cameron; Adjerid, Idris; Ball, Sheryl; Dogan, Can
• 署名单位: University of Notre Dame; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Radford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.03510
• 发表日期: 2025
• 关键词: algorithm adoption; Artificial intelligence; behavioral decision making; Experimental economics; Betrayal aversion

摘要

Failing to follow expert advice can have real and dangerous consequences. While any number of factors may lead a decision maker to refuse expert advice, the proliferation of algorithmic experts has further complicated the issue. One potential mechanism that restricts the acceptance of expert advice is betrayal aversion, or the strong dislike for the violation of trust norms. This study explores whether the introduction of expert algorithms in place of human experts can attenuate betrayal aversion and lead to higher overall rates of seeking expert advice. In other words, we ask: are decision makers averse to algorithmic betrayal? The answer to this question is uncertain ex ante. We answer this question through an experimental financial market where there is an identical risk of betrayal from either a human or algorithmic financial advisor. We find that the willingness to delegate to human experts is significantly reduced by betrayal aversion, while no betrayal aversion is exhibited toward algorithmic experts. The impact of betrayal aversion toward financial advisors is considerable: the resulting unwillingness to take the advice of the human expert leads to a 20% decrease in subsequent earnings, while no loss in earnings is observed in the algorithmic expert condition. This study has significant implications for firms, policymakers, and consumers, specifically in the financial services industry.