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.

机器学习方法在临床决策中的重要性日益提升，因此解决公平性问题变得越来越迫切。尽管已经有很多工作致力于检测和改善算法偏见，但目前的方法存在缺陷和潜在危害。我们提出因果角度来研究算法偏见，强调数据集偏见的不同来源可能看起来相似，但需要采用截然不同的缓解策略。我们从盛行度、呈现度和注释差异三个因果偏见机制的角度进行分析。我们提供了一个实用的三步骤框架，用于思考医学影像公平性，支持开发安全和公正的AI预测模型。

非公平的午餐：医学影像机器学习数据集偏差的因果观点