When Algorithms Err: Differential Impact of Early vs. Late Errors on Users’ Reliance on Algorithms

Abstract

Errors are a natural part of predictive algorithms, but may discourage users from relying on algorithms. We conduct two experiments to demonstrate that reliance on a predictive algorithm following a substantial error is affected by (i) when the error occurs and (ii) how the algorithm is used in the decision-making process. We find that the impact of an error on reliance depends on whether the error occurs early (i.e., when users first start using the algorithm) or late (i.e., after users have used the algorithm for an extended period). While an early error results in substantial and persistent reliance reduction, a late error affects reliance only temporarily and to a lesser extent. However, when users have more control over how to use the algorithm's predictions, error timing ceases to have a significant impact. Our work advances the understanding of algorithm aversion and informs the practical design of algorithmic decision-making systems.