  Chronic pain significantly diminishes the quality of life for millions
worldwide. While psychoeducation and therapy can improve pain outcomes, many
individuals experiencing pain lack access to evidence-based treatments or fail
to complete the necessary number of sessions to achieve benefit. Reinforcement
learning (RL) shows potential in tailoring personalized pain management
interventions according to patients' individual needs while ensuring the
efficient use of scarce clinical resources. However, clinicians, patients, and
healthcare decision-makers are concerned that RL solutions could exacerbate
disparities associated with patient characteristics like race or gender. In
this article, we study gender fairness in personalized pain care
recommendations using a real-world application of reinforcement learning
(Piette et al., 2022a). Here, adhering to gender fairness translates to minimal
or no disparity in the utility received by subpopulations as defined by gender.
We investigate whether the selection of relevant patient information (referred
to as features) used to assist decision-making affects gender fairness. Our
experiments, conducted using real-world data Piette, 2022), indicate that
included features can impact gender fairness. Moreover, we propose an RL
solution, NestedRecommendation, that demonstrates the ability: i) to adaptively
learn to select the features that optimize for utility and fairness, and ii) to
accelerate feature selection and in turn, improve pain care recommendations
from early on, by leveraging clinicians' domain expertise.
