  Restless multi-armed bandits (RMABs) are a popular framework for algorithmic
decision making in sequential settings with limited resources. RMABs are
increasingly being used for sensitive decisions such as in public health,
treatment scheduling, anti-poaching, and -- the motivation for this work --
digital health. For such high stakes settings, decisions must both improve
outcomes and prevent disparities between groups (e.g., ensure health equity).
We study equitable objectives for RMABs (ERMABs) for the first time. We
consider two equity-aligned objectives from the fairness literature, minimax
reward and max Nash welfare. We develop efficient algorithms for solving each
-- a water filling algorithm for the former, and a greedy algorithm with
theoretically motivated nuance to balance disparate group sizes for the latter.
Finally, we demonstrate across three simulation domains, including a new
digital health model, that our approaches can be multiple times more equitable
than the current state of the art without drastic sacrifices to utility. Our
findings underscore our work's urgency as RMABs permeate into systems that
impact human and wildlife outcomes. Code is available at
https://github.com/google-research/socialgood/tree/equitable-rmab
