  We propose a novel method for enforcing AI fairness with respect to protected
or sensitive factors. This method uses a dual strategy performing training and
representation alteration (TARA) for the mitigation of prominent causes of AI
bias by including: a) the use of representation learning alteration via
adversarial independence to suppress the bias-inducing dependence of the data
representation from protected factors; and b) training set alteration via
intelligent augmentation to address bias-causing data imbalance, by using
generative models that allow the fine control of sensitive factors related to
underrepresented populations via domain adaptation and latent space
manipulation. When testing our methods on image analytics, experiments
demonstrate that TARA significantly or fully debiases baseline models while
outperforming competing debiasing methods that have the same amount of
information, e.g., with (% overall accuracy, % accuracy gap) = (78.8, 0.5) vs.
the baseline method's score of (71.8, 10.5) for EyePACS, and (73.7, 11.8) vs.
(69.1, 21.7) for CelebA. Furthermore, recognizing certain limitations in
current metrics used for assessing debiasing performance, we propose novel
conjunctive debiasing metrics. Our experiments also demonstrate the ability of
these novel metrics in assessing the Pareto efficiency of the proposed methods.

Machine Learning;Computer Vision and Pattern Recognition