Bias in Reinforcement Learning: A Review in Healthcare Applications

Abstract

Reinforcement learning (RL) can assist in medical decision making using patient data collected in electronic health record (EHR) systems. RL, a type of machine learning, can use these data to develop treatment policies. However, RL models are typically trained using imperfect retrospective EHR data. Therefore, if care is not taken in training, RL policies can propagate existing bias in healthcare. Literature that considers and addresses the issues of bias and fairness in sequential decision making are reviewed. The major themes to mitigate bias that emerge relate to (1) data management; (2) algorithmic design; and (3) clinical understanding of the resulting policies.

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