  Fairness is becoming an increasingly crucial issue for computer vision,
especially in the human-related decision systems. However, achieving
algorithmic fairness, which makes a model produce indiscriminative outcomes
against protected groups, is still an unresolved problem. In this paper, we
devise a systematic approach which reduces algorithmic biases via feature
distillation for visual recognition tasks, dubbed as MMD-based Fair
Distillation (MFD). While the distillation technique has been widely used in
general to improve the prediction accuracy, to the best of our knowledge, there
has been no explicit work that also tries to improve fairness via distillation.
Furthermore, We give a theoretical justification of our MFD on the effect of
knowledge distillation and fairness. Throughout the extensive experiments, we
show our MFD significantly mitigates the bias against specific minorities
without any loss of the accuracy on both synthetic and real-world face
datasets.
