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A causal perspective on dataset bias in machine learning for medical imaging - Nature Machine Intelligence
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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.
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