Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2022

Abstract

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention decisions. To achieve this goal, we formally define the fairness constraints in RMAB and provide planning and learning methods to solve RMAB in a fair manner. We demonstrate key theoretical properties of fair RMAB and experimentally demonstrate that our proposed methods handle fairness constraints without sacrificing significantly on solution quality.

Keywords

Restless multi-armed bandits, Fairness constraints, Whittle index, Q learning

Discipline

Information Security

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 2022 Aug 1-5

First Page

1158

Last Page

1167

ISBN

2640-3498

Publisher

Proceedings of Machine Learning Research

City or Country

Netherlands

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