In this lab, we'll investigate one recently published approach to addressing algorithmic bias. We'll build a facial detection model that learns the latent variables underlying face image datasets and uses this to adaptively re-sample the training data, thus mitigating any biases that may be present in order to train a debiased model.
The following picture compares the accuracy on each of "Dark Male", "Dark Female", "Light Male", and "Light Female" demographics being classified as a face using a standard CNN classifier and using our debiased model.
