A causal perspective on dataset bias in machine learning for medical imaging

As machine learning methods gain prominence within clinical decision-making, the need to address 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 seem 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 predictive models. Machine learning algorithms play important roles in medical imaging analysis but can be affected by biases in training data. Jones and colleagues discuss how causal reasoning can be used to better understand and tackle algorithmic bias in medical imaging analysis.