Along with the increasing availability of health data has come the rise of data-driven models to inform decision-making and policy. These models have the potential to benefit both patients and health care providers but can also exacerbate health inequities. Existing "algorithmic fairness" methods for measuring and correcting model bias fall short of what is needed for health policy in two key ways. First, methods typically focus on a single grouping along which discrimination may occur rather than considering multiple, intersecting groups. Second, in clinical applications, risk prediction is typically used to guide treatment, creating distinct statistical issues that invalidate most existing techniques. We present summary unfairness metrics that build on existing techniques in "counterfactual fairness" to address both challenges. We also develop a complete framework of estimation and inference tools for our metrics, including the unfairness value ("u-value"), used to determine the relative extremity of unfairness, and standard errors and confidence intervals employing an alternative to the standard bootstrap. We demonstrate application of our framework to a COVID-19 risk prediction model deployed in a major Midwestern health system.

翻译：随着健康数据日益可得，基于数据驱动的模型在辅助决策与政策制定方面随之兴起。这些模型有望惠及患者和医疗服务提供者，但也可能加剧健康不平等。现有的用于衡量和纠正模型偏差的"算法公平性"方法在两方面无法满足健康政策的需求。首先，这些方法通常仅关注单一维度的歧视分组，而非考虑多个交叉群体。其次，在临床应用场景中，风险预测通常用于指导治疗，这使得多数现有技术因独特的统计问题而失效。我们提出了基于"反事实公平性"现有技术的总结性不公平性指标，以应对这两个挑战。我们还为这些指标开发了一套完整的估计与推断工具，包括用于衡量不公平性相对极端程度的"不公平值"（u值），以及采用自助法替代方案的标准误与置信区间。我们展示了该框架在部署于美国中西部某主要卫生系统的新冠肺炎风险预测模型中的应用。