Generative Counterfactual Augmentation for Bias Mitigation

Jason Uwaeze, Pranav Kulkarni, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 1164-1171

Deep learning (DL) models trained for chest x-ray (CXR) classification can encode protected demographic attributes and exhibit bias towards underrepresented patient populations. In this work, we propose Generative Counterfactual Augmentation (GCA), a framework for mitigating algorithmic bias through demographic-complete augmentation of training data. We use a StyleGAN3-based synthesis network and SVM-guided latent space traversal to generate structured age and sex counterfactuals for each CXR while preserving disease features. We extensively evaluate GCA for training DL models with the RSNA Pneumonia dataset using controlled underdiagnosis bias injection across age- and sex-groups at varying rates. Our results show up to 23% reduction in FNR disparity, with a mean reduction of 9%, across varying rates of underdiagnosis bias. When evaluated with the external CheXpert and MIMIC-CXR datasets, we observe a consistent FNR reduction and improved model generalizability. We demonstrate that GCA is an effective strategy for mitigating algorithmic bias in DL models for medical imaging, ensuring trustworthiness in clinical settings. Our code is available at https://github.com/Wazhee/GCA