  Machine learning fairness concerns about the biases towards certain protected
or sensitive group of people when addressing the target tasks. This paper
studies the debiasing problem in the context of image classification tasks. Our
data analysis on facial attribute recognition demonstrates (1) the attribution
of model bias from imbalanced training data distribution and (2) the potential
of adversarial examples in balancing data distribution. We are thus motivated
to employ adversarial example to augment the training data for visual
debiasing. Specifically, to ensure the adversarial generalization as well as
cross-task transferability, we propose to couple the operations of target task
classifier training, bias task classifier training, and adversarial example
generation. The generated adversarial examples supplement the target task
training dataset via balancing the distribution over bias variables in an
online fashion. Results on simulated and real-world debiasing experiments
demonstrate the effectiveness of the proposed solution in simultaneously
improving model accuracy and fairness. Preliminary experiment on few-shot
learning further shows the potential of adversarial attack-based pseudo sample
generation as alternative solution to make up for the training data lackage.
