AI Oversight and Human Mistakes: Evidence from Centre Court

TL;DR

This study provides field evidence that AI oversight influences human decision-making, reducing overall errors but increasing certain types of mistakes, with psychological costs affecting decision priorities.

Contribution

First empirical evidence showing AI oversight impacts human decisions in a high-stakes setting, highlighting shifts in error types and psychological effects on decision-makers.

Findings

Umpires reduced overall mistake rate after Hawk-Eye introduction.

Umpires increased calls for balls in, shifting from Type II to Type I errors.

Umpires cared 37% more about Type II errors under AI oversight.

Abstract

Powered by the increasing predictive capabilities of machine learning algorithms, artificial intelligence (AI) systems have the potential to overrule human mistakes in many settings. We provide the first field evidence that the use of AI oversight can impact human decision-making. We investigate one of the highest visibility settings where AI oversight has occurred: Hawk-Eye review of umpires in top tennis tournaments. We find that umpires lowered their overall mistake rate after the introduction of Hawk-Eye review, but also that umpires increased the rate at which they called balls in, producing a shift from making Type II errors (calling a ball out when in) to Type I errors (calling a ball in when out). We structurally estimate the psychological costs of being overruled by AI using a model of attention-constrained umpires, and our results suggest that because of these costs, umpires cared 37% more about Type II errors under AI oversight.