# AI Oversight and Human Mistakes: Evidence from Centre Court

arXiv: https://arxiv.org/abs/2401.16754 · HTML: https://mlsanity.com/paper/2401.16754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [The Algorithmic Automation Problem: Prediction, Triage, and Human Effort](https://mlsanity.com/paper/1903.12220/) (2019)
- [On the Fairness of Machine-Assisted Human Decisions](https://mlsanity.com/paper/2110.15310/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Heterogeneous Productivity Effects of Generative AI](https://mlsanity.com/paper/2403.01964/) (2024)
- [AI Recommendations and Non-instrumental Image Concerns](https://mlsanity.com/paper/2504.19047/) (2025)

## Abstract (arXiv)

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.