  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.
