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As machine learning methods gain prominence within clinical decision-making,addressing fairness concerns becomes increasingly urgent. Despite considerablework dedicated to detecting and ameliorating algorithmic bias, today's methodsare deficient with potentially harmful consequences. Our causal perspectivesheds new light on algorithmic bias, highlighting how different sources ofdataset bias may appear indistinguishable yet require substantially differentmitigation strategies. We introduce three families of causal bias mechanismsstemming from disparities in prevalence, presentation, and annotation. Ourcausal analysis underscores how current mitigation methods tackle only a narrowand often unrealistic subset of scenarios. We provide a practical three-stepframework for reasoning about fairness in medical imaging, supporting thedevelopment of safe and equitable AI prediction models.

No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging