Human Responses to Algorithmic Correction: Evidence from Centre Court

Abstract:We study how algorithmic review changes the initial decisions that a system is designed to correct. Using Hawk-Eye data from professional tennis, we compare umpire decisions with and without review against a common accuracy benchmark. Under review, initial errors were 8.6 percentage points higher just outside the line and lower for comparably close balls inside. We find no evidence that average initial accuracy deteriorated. Within matches, reversals were followed by larger adjustments than upheld calls, consistent with adaptation to correction. Overall, correction offsets worse initial decisions for incorrect in calls and reinforces better initial decisions for incorrect out calls. Whether review lowers organizational costs depends jointly on these changes and the costs of remedying corrected errors. A conditional illustration shows how utilization spreads fixed review costs across calls.

Machine Learning (cs.LG); Computers and Society (cs.CY); General Economics (econ.GN)

arXiv:2401.16754 [cs.LG]

(or arXiv:2401.16754v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2401.16754 Focus to learn more arXiv-issued DOI via DataCite

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