Group Fairness in Predict-Then-Optimize Settings for Restless Bandits

Restless multi-arm bandits, a class of resource allocation problems involving multiple agents with a resource constraint, have applications in healthcare, machine maintenance, and anti-poaching. For such high stake use cases, decisions must ensure equity among groups. Prior RMAB research suffers from several limitations, e.g., it fails to address equitable RMABs with guarantees on resources given to different groups, or fails to consider practical settings where transition dynamics are unknown. We address these limitations by developing
a decision-focused-learning pipeline to solve equitable RMABs, using a novel budget allocation algorithm to prevent disparity between groups. Our results on both synthetic data and real-world public health data demonstrate that our approach greatly improves equity without sacrificing utility.