Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias

• URL: http://arxiv.org/abs/2412.04739v1
• Date: Fri, 06 Dec 2024 02:59:36 GMT
• Title: Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias
• Authors: Bowei Tian, Yexiao He, Meng Liu, Yucong Dai, Ziyao Wang, Shwai He, Guoheng Sun, Zheyu Shen, Wanghao Ye, Yongkai Wu, Ang Li,
• Abstract summary: In medical image analysis, model predictions can be affected by sensitive attributes, such as race and gender. We present a causal modeling framework, which aims to reduce the impact of sensitive attributes on diagnostic predictions.
• Score: 14.848344916632024
• License:
• Abstract: In medical image analysis, model predictions can be affected by sensitive attributes, such as race and gender, leading to fairness concerns and potential biases in diagnostic outcomes. To mitigate this, we present a causal modeling framework, which aims to reduce the impact of sensitive attributes on diagnostic predictions. Our approach introduces a novel fairness criterion, \textbf{Diagnosis Fairness}, and a unique fairness metric, leveraging path-specific fairness to control the influence of demographic attributes, ensuring that predictions are primarily informed by clinically relevant features rather than sensitive attributes. By incorporating adversarial perturbation masks, our framework directs the model to focus on critical image regions, suppressing bias-inducing information. Experimental results across multiple datasets demonstrate that our framework effectively reduces bias directly associated with sensitive attributes while preserving diagnostic accuracy. Our findings suggest that causal modeling can enhance both fairness and interpretability in AI-powered clinical decision support systems.

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