An ExplainableFair Framework for Prediction of Substance Use Disorder
  Treatment Completion
Mary M. Lucas Xiaoyang Wang Chia-Hsuan Chang Christopher C. Yang Jacqueline E. Braughton Quyen M. Ngo
Fairness of machine learning models in healthcare has drawn increasing
attention from clinicians, researchers, and even at the highest level of
government. On the other hand, the importance of developing and deploying
interpretable or explainable models has been demonstrated, and is essential to
increasing the trustworthiness and likelihood of adoption of these models. The
objective of this study was to develop and implement a framework for addressing
both these issues - fairness and explainability. We propose an explainable
fairness framework, first developing a model with optimized performance, and
then using an in-processing approach to mitigate model biases relative to the
sensitive attributes of race and sex. We then explore and visualize
explanations of the model changes that lead to the fairness enhancement process
through exploring the changes in importance of features. Our resulting-fairness
enhanced models retain high sensitivity with improved fairness and explanations
of the fairness-enhancement that may provide helpful insights for healthcare
providers to guide clinical decision-making and resource allocation.