Algorithm Aversion: Evidence from Ridesharing Drivers

• 成果类型: Article; Early Access
• 署名作者: Liu, Meng; Tang, Xiaocheng; Xia, Siyuan; Zhang, Shuo; Zhu, Yuting; Meng, Qianying
• 署名单位: Washington University (WUSTL); Shanghai Jiao Tong University; National University of Singapore
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.02475
• 发表日期: 2023
• 关键词: algorithm aversion; AI algorithms; contextual experience; herding; ridesharing

摘要

The low rate of adoption by human users often hinders AI algorithms from achieving their intended efficiency gains. This is particularly true for algorithms that prioritize system-wide objectives because they can create misalignment of incentives and cause confusion among potential users. We provide one of the first large-scale field studies on algorithm aversion by leveraging an algorithmic recommendation rollout on a large ride sharing platform. We identify contextual experience and herding as two important factors that explain ridesharing drivers' aversion to an algorithm that is designed to help drivers make better location choices. Specifically, we find that drivers are less likely to follow the algorithm when the algorithmic recommendation does not align with their past experience at a given location-time unit and when their peers' actions contradict the algorithmic recommendations. We discuss the managerial implications of these findings.