Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias

TL;DR

This paper introduces a causal modeling framework with a novel fairness criterion and metric to reduce bias from sensitive attributes in medical diagnosis, improving fairness without sacrificing accuracy.

Contribution

It proposes a new causal modeling approach with path-specific fairness and adversarial masks to mitigate bias in medical image analysis.

Findings

Effective bias reduction across multiple datasets

Maintains diagnostic accuracy while improving fairness

Enhances interpretability of AI diagnostic models

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.