An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion

TL;DR

This paper introduces an explainable fairness framework for predicting substance use disorder treatment completion, combining bias mitigation and interpretability to improve trust and clinical utility.

Contribution

It develops a novel in-processing approach that enhances fairness and explainability in healthcare prediction models, addressing race and sex biases.

Findings

Models with fairness enhancements retain high sensitivity.

Fairness improvements are interpretable through feature importance visualization.

The framework supports clinical decision-making and resource allocation.

Abstract

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.