Algorithm Aversion: Evidence from Ridesharing Drivers
36 Pages Posted: 30 Aug 2022 Last revised: 19 Jun 2023
Date Written: August 19, 2022
AI algorithms often cannot realize their intended efficiency gains because of their low adoption by human users. Leveraging an algorithmic recommendation rollout on a large ridesharing platform, we identify contextual experience and herding as two important factors that explain ridesharing drivers’ aversion to an algorithm that conducts system-wide optimization and 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.
Keywords: Algorithm Aversion, AI Algorithms, Contextual Experience, Herding, Ridesharing
Suggested Citation: Suggested Citation