TOWARDS FAIRNESS CONSTRAINED RESTLESS MULTI-ARMED BANDITS: A CASE STUDY OF MATERNAL AND CHILD CARE DOMAIN
Keywords: restless multi-armed bandits, fairness, kl divergence
Abstract: Restless multi-armed bandits (RMABs) are widely used for resource allocation in dynamic environments, but they typically do not consider fairness implications. This paper introduces a fairness-aware approach for offline RMABs. We propose a Kullback-Leibler (KL) divergence-based fairness metric to quantify the discrepancy between the selected and the overall population. This is incorporated as a regularizer into the soft whittle index optimization. We evaluate our fairness-aware algorithm on a real-world RMAB dataset where initial results suggest that our approach can potentially improve fairness while preserving solution quality.
Submission Number: 221
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