Generative Models Improve Fairness Of Medical Classifiers Under Distribution Shifts

This article discusses the potential for using generative models to improve fairness and generalization in A.I. systems. The authors also address challenges such as reducing memorization, providing privacy guarantees, and accounting for unobserved characteristics. They also describe the datasets used in their research, which were obtained with informed consent from participants in previous studies.