Fair Best Arm Identification with Fixed Confidence
<|reference_start|>Fair Best Arm Identification with Fixed Confidence: In this work, we present a novel framework for Best Arm Identification (BAI) under fairness constraints, a setting that we refer to as \textit{F-BAI} (fair BAI). Unlike traditional BAI, which solely focuses on identifying the optimal arm with minimal sample complexity, F-BAI also includes a set of fairness constraints. These constraints impose a lower limit on the selection rate of each arm and can be either model-agnostic or model-dependent. For this setting, we establish an instance-specific sample complexity lower bound and analyze the \textit{price of fairness}, quantifying how fairness impacts sample complexity. Based on the sample complexity lower bound, we propose F-TaS, an algorithm provably matching the sample complexity lower bound, while ensuring that the fairness constraints are satisfied. Numerical results, conducted using both a synthetic model and a practical wireless scheduling application, show the efficiency of F-TaS in minimizing the sample complexity while achieving low fairness violations.<|reference_end|>
@article{russo2024fair,
    title={Fair Best Arm Identification with Fixed Confidence},
    author={Alessio Russo, Filippo Vannella},
    journal={arXiv preprint arXiv:2408.17313},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17313},
    primaryClass={cs.LG cs.AI}
}