Counterfactual Reasoning for Fair Clinical Risk Prediction

14 Jul 2019Stephen PfohlTony DuanDaisy Yi DingNigam H. Shah

The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level... (read more)

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