  Recent work shows unequal performance of commercial face classification
services in the gender classification task across intersectional groups defined
by skin type and gender. Accuracy on dark-skinned females is significantly
worse than on any other group. In this paper, we conduct several analyses to
try to uncover the reason for this gap. The main finding, perhaps surprisingly,
is that skin type is not the driver. This conclusion is reached via stability
experiments that vary an image's skin type via color-theoretic methods, namely
luminance mode-shift and optimal transport. A second suspect, hair length, is
also shown not to be the driver via experiments on face images cropped to
exclude the hair. Finally, using contrastive post-hoc explanation techniques
for neural networks, we bring forth evidence suggesting that differences in
lip, eye and cheek structure across ethnicity lead to the differences. Further,
lip and eye makeup are seen as strong predictors for a female face, which is a
troubling propagation of a gender stereotype.
