Algorithm Aversion: Evidence from Ridesharing Drivers

AI algorithms often cannot realize their intended efficiency gains because of their low adoption by human users. We uncover various factors that explain ridesharing drivers’ aversion to an algorithm designed to help them make better location choices. By leveraging an algorithmic recommendation rollout on a large ridesharing platform, we find that drivers are more averse to the algorithm when they face a higher cost of implementing its instructions, when their experience suggests a greater opportunity cost of following the algorithm, and when their peers’ actions contradict the algorithmic recommendations. We discuss the managerial implications of these findings.