Enhancement of Fairness in AI for Chest X-ray Classification

Presentation Time: 09:45 AM - 10:00 AM

Abstract Keywords: Fairness and Elimination of Bias, Machine Learning, Racial disparities
Primary Track: Foundations

The use of artificial intelligence (AI) in medicine has shown promise to improve the quality of healthcare decisions. However, AI can be biased in a manner that produces unfair predictions for certain demographic subgroups. In MIMIC-CXR, a publicly available dataset of over 300,000 chest X-ray images, diagnostic AI has been shown to have a higher false negative rate for racial minorities. We evaluated the capacity of synthetic data augmentation, oversampling, and demographic-based corrections to enhance the fairness of AI predictions. We show that adjusting unfair predictions for demographic attributes, such as race, is ineffective at improving fairness or predictive performance. However, using oversampling and synthetic data augmentation to modify disease prevalence reduced such disparities by 65.4% and 10.6%, respectively. Moreover, such fairness gains were accomplished without reduction in performance (95% CI AUC: [0.816, 0.820] versus [0.817, 0.820] versus [0.817, 0.821] for baseline, oversampling, and augmentation, respectively).

Speaker(s):
Nicholas Jackson, PhD Student Biomedical Informatics
Vanderbilt University

Author(s):
Nicholas Jackson, PhD Student Biomedical Informatics - Vanderbilt University; Chao Yan, PhD - Vanderbilt University Medical Center; Bradley Malin, PhD - Vanderbilt University Medical Center;