Deep learning has revolutionized computer vision, driving face biometric systems to unprecedented levels of accuracy, but also introducing systematic errors that remain difficult to comprehend. This talk explores recent advances in the study of bias in deep face models, showing how disparities arise not just in final decisions, but within the deep feature representations themselves, spanning the learned latent space, neuron activations, and network parameters.