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.
翻译:利用机器学习来指导临床决策,有可能加剧现有的健康差异。最近的一些工作将这一问题视为算法公平问题,而算法公平是引起相当关注和批评的一个框架。然而,由于道德和技术方面的考虑,这一框架是否适当尚不明确,这些考虑包括公平措施与模型性能之间的权衡,而对于临床结果的预测模型并不十分了解。为了为正在进行的辩论提供信息,我们开展了一项实证研究,以说明惩罚集体公平违规行为对一系列示范业绩和群体公平措施的影响。我们重复了多种观察保健数据库、临床结果和敏感属性的分析。我们发现,惩罚群体间预测分布差异的程序几乎普遍导致群体内多种业绩指标的退化。在审查这些程序的次级影响时,我们注意到这些程序对不同实验条件之间校正和排名的公平措施的影响。除了报告的权衡,我们强调,对保健的算法公平性分析缺乏必要的背景和因果意识,以致无法了解导致健康公平机制的外部公平性。我们利用这些分析机制来抵消机理学上的差异,同时利用这些机理学分析模型,以便利用这些分析机制来消除机理学上的差异。