Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare
This work addresses fairness in high-stakes healthcare applications, offering an incremental improvement by balancing risk disparities while adhering to non-maleficence principles.
The paper tackles the problem of minimizing performance disparities across sensitive groups in healthcare without causing unnecessary harm, by formalizing a Pareto-optimal approach and demonstrating it on ICU mortality prediction and skin lesion classification with improved fairness metrics.
Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-populations. We are interested in learning models that minimize performance discrepancies across sensitive groups without causing unnecessary harm. This is relevant to high-stakes domains such as healthcare, where non-maleficence is a core principle. We formalize this objective using Pareto frontiers, and provide analysis, based on recent works in fairness, to exemplify scenarios were perfect fairness might not be feasible without doing unnecessary harm. We present a methodology for training neural networks that achieve our goal by dynamically re-balancing subgroups risks. We argue that even in domains where fairness at cost is required, finding a non-unnecessary-harm fairness model is the optimal initial step. We demonstrate this methodology on real case-studies of predicting ICU patient mortality, and classifying skin lesions from dermatoscopic images.