The Implementation Cost of Fairness in Service Policy Selection
Abstract
Service organizations use simulation, pilot studies, and historical data to select service policies that balance aggregate performance and fairness. Existing work primarily evaluates the operational cost of fairness, defined as the performance loss caused by restricting the feasible policy set. In this research, we identify and study implementation cost as a distinct and equally important dimension of fairness, defined as the sampling effort required to verify fairness and reliably select the best fair policy. Specifically, we consider fixed-budget policy selection under fairness requirements based on mean performance, under-service risk, quantiles, and upper tail outcomes, and show that different fairness metrics can induce the same fair policy set and the same operational cost while requiring substantially different amounts of evidence because their estimators have different local verification difficulty. We further show that, near a critical fairness tolerance, the required sampling budget for implementation scales inversely with the square of the distance to the boundary, creating a finite-budget implementation gap. These results imply that the fairness metric, the fairness tolerance, and the available sampling budget should be designed jointly, because a population-level fairness requirement may be operationally attractive but not statistically implementable at the available budget. To translate this perspective into practice, we develop a fairness-guided adaptive allocation algorithm that directs samples toward the ranking and fairness verification comparisons governing false selection. Experiments in synthetic, call center, and emergency department settings demonstrate the implementation cost mechanisms identified in this work and show that the proposed allocation uses the sampling budget much more effectively.
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BibTeX
@misc{liu2026implementation,
title = {{The Implementation Cost of Fairness in Service Policy Selection}},
author = {Junjie Liu and Mingjie Hu and Kejia Hu and Siyang Gao and Jianqiang Hu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.24072},
eprint = {2609.24072},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.24072},
}
RIS
TY - UNPB
TI - The Implementation Cost of Fairness in Service Policy Selection
AU - Junjie Liu
AU - Mingjie Hu
AU - Kejia Hu
AU - Siyang Gao
AU - Jianqiang Hu
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.24072
UR - https://arxiv.org/abs/2609.24072
AB - Service organizations use simulation, pilot studies, and historical data to select service policies that balance aggregate performance and fairness. Existing work primarily evaluates the operational cost of fairness, defined as the performance loss caused by restricting the feasible policy set. In this research, we identify and study implementation cost as a distinct and equally important dimension of fairness, defined as the sampling effort required to verify fairness and reliably select the best fair policy. Specifically, we consider fixed-budget policy selection under fairness requirements based on mean performance, under-service risk, quantiles, and upper tail outcomes, and show that different fairness metrics can induce the same fair policy set and the same operational cost while requiring substantially different amounts of evidence because their estimators have different local verification difficulty. We further show that, near a critical fairness tolerance, the required sampling budget for implementation scales inversely with the square of the distance to the boundary, creating a finite-budget implementation gap. These results imply that the fairness metric, the fairness tolerance, and the available sampling budget should be designed jointly, because a population-level fairness requirement may be operationally attractive but not statistically implementable at the available budget. To translate this perspective into practice, we develop a fairness-guided adaptive allocation algorithm that directs samples toward the ranking and fairness verification comparisons governing false selection. Experiments in synthetic, call center, and emergency department settings demonstrate the implementation cost mechanisms identified in this work and show that the proposed allocation uses the sampling budget much more effectively.
ER -