As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent. Despite considerable work dedicated to detecting and ameliorating algorithmic bias, today's methods are deficient with potentially harmful consequences. Our causal perspective sheds new light on algorithmic bias, highlighting how different sources of dataset bias may appear indistinguishable yet require substantially different mitigation strategies. We introduce three families of causal bias mechanisms stemming from disparities in prevalence, presentation, and annotation. Our causal analysis underscores how current mitigation methods tackle only a narrow and often unrealistic subset of scenarios. We provide a practical three-step framework for reasoning about fairness in medical imaging, supporting the development of safe and equitable AI prediction models.

翻译：随着机器学习方法在临床决策中日益重要，解决公平性问题变得愈发紧迫。尽管已有大量工作致力于检测和缓解算法偏差，但当前方法仍存在缺陷且可能带来有害后果。我们的因果视角为算法偏差提供了全新见解，揭示了不同来源的数据集偏差看似难以区分，却需要截然不同的缓解策略。我们提出三类因果偏差机制，其源于患病率、呈现方式及标注的差异。因果分析强调，当前缓解方法仅处理了狭窄且往往不切实际的场景子集。我们提供了实用的三步框架以审视医学成像中的公平性问题，支持开发安全且公平的AI预测模型。