An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction

TL;DR

This empirical study investigates how penalizing group fairness violations in clinical risk prediction models affects performance and fairness, revealing widespread performance degradation and emphasizing the need for contextual understanding beyond fairness metrics.

Contribution

The paper provides a comprehensive empirical analysis of fairness interventions in healthcare ML models, highlighting their limitations and advocating for broader sociotechnical considerations.

Findings

Fairness penalties often degrade model performance across multiple metrics.

Effects on fairness measures vary across different conditions and attributes.

Contextual and causal understanding is crucial for fair healthcare ML applications.

Abstract

The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities. Several recent works frame the problem as that of algorithmic fairness, a framework that has attracted considerable attention and criticism. However, the appropriateness of this framework is unclear due to both ethical as well as technical considerations, the latter of which include trade-offs between measures of fairness and model performance that are not well-understood for predictive models of clinical outcomes. To inform the ongoing debate, we conduct an empirical study to characterize the impact of penalizing group fairness violations on an array of measures of model performance and group fairness. We repeat the analyses across multiple observational healthcare databases, clinical outcomes, and sensitive attributes. We find that procedures that penalize differences between the distributions of predictions across groups induce nearly-universal degradation of multiple performance metrics within groups. On examining the secondary impact of these procedures, we observe heterogeneity of the effect of these procedures on measures of fairness in calibration and ranking across experimental conditions. Beyond the reported trade-offs, we emphasize that analyses of algorithmic fairness in healthcare lack the contextual grounding and causal awareness necessary to reason about the mechanisms that lead to health disparities, as well as about the potential of algorithmic fairness methods to counteract those mechanisms. In light of these limitations, we encourage researchers building predictive models for clinical use to step outside the algorithmic fairness frame and engage critically with the broader sociotechnical context surrounding the use of machine learning in healthcare.