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. We do so by
requiring that the same prediction be made for a patient, and a counterfactual
patient resulting from changing a sensitive attribute, if the factual and
counterfactual outcomes do not differ. We investigate the extent to which the
augmented counterfactual fairness criteria may be applied to develop fair
models for prolonged inpatient length of stay and mortality with observational
electronic health records data. As the fairness criteria is ill-defined without
knowledge of the data generating process, we use a variational autoencoder to
perform counterfactual inference in the context of an assumed causal graph.
While our technique provides a means to trade off maintenance of fairness with
reduction in predictive performance in the context of a learned generative
model, further work is needed to assess the generality of this approach.

本文使用机器学习系统支持决策制定在医疗保健中的问答涉及使用电子医疗记录中的观察性数据中隐含的偏见，进而进一步发展了组公平性标准，以在个体层面上扩展增益计数事实公正标准，探讨扩增的标准是否可以应用于使用观测性电子健康记录数据的病人长住和死亡的公平模型的开发。通过一个假设因果图的变分自动编码器来执行反事实推理，提供了一种在学习生成模型的背景下，在公平维护与预测性能下降之间权衡的方法。