  Recent work demonstrates that images from various chest X-ray datasets
contain visual features that are strongly correlated with protected demographic
attributes like race and gender. This finding raises issues of fairness, since
some of these factors may be used by downstream algorithms for clinical
predictions. In this work, we propose a framework, using generative adversarial
networks (GANs), to visualize what features are most different between X-rays
belonging to two demographic subgroups.
