  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.
