This is a very surprising result, putting it mildly! Edit: Upon reflection, the performance as a function of image degradation is not that surprising given wha

This is a very surprising result, putting it mildly! Edit: Upon reflection, the performance as a function of image degradation is not that surprising given what we know about the sensitivity of neural networks to slight perturbations. My best guess at this point is that while humans can detect other features like breast density/bone density/BMI from the scan, they don't automatically interpret race as a function of these, while of course the NN does. The fact that it does so much better than the direct regression between these features and race (e.g. .55 AUC predicting race from BMI, .54 AUC predicting race from breast density) is initially very surprising. But they don't report the results of a similar experiment using all of these together. I suspect that simply predicting race from BMI + Breast Density + Age + Bone Density + Sex would achieve similar performance.