2024/08/30 by Alessio Russo, Russo, Alessio, Filippo Vannella +1 · 1 citation
Economics, Econometrics and Finance · Social Sciences · #Artificial Intelligence (cs.AI) #Criminal Law and Evidence #FOS: Computer and information sciences #Law, Economics, and Judicial Systems #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2408.17313
openalex publication_date 2024/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we present a novel framework for Best Arm Identification (BAI) under fairness constraints, a setting that we refer to as 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 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.