Detecting Bias with Generative Counterfactual Face Attribute Augmentation
We introduce a simple framework for identifying biases of a smiling attribute classifier. Our method poses counterfactual questions of the form: how would the prediction change if this face characteristic had been different? We leverage recent advances in generative adversarial networks to build a realistic generative model of face images that affords controlled manipulation of specific image characteristics. We introduce a set of metrics that measure the effect of manipulating a specific property of an image on the output of a trained classifier. Empirically, we identify several different factors of variation that affect the predictions of a smiling classifier trained on CelebA.