An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
Stephen R. Pfohl,
Agata Foryciarz,
Nigam H. Shah
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… Show more
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