  The widespread use of automated decision processes in many areas of our
society raises serious ethical issues concerning the fairness of the process
and the possible resulting discriminations. In this work, we propose a novel
approach called GANsan whose objective is to prevent the possibility of any
discrimination i.e., direct and indirect) based on a sensitive attribute by
removing the attribute itself as well as the existing correlations with the
remaining attributes. Our sanitization algorithm GANsan is partially inspired
by the powerful framework of generative adversarial networks (in particular the
Cycle-GANs), which offers a flexible way to learn a distribution empirically or
to translate between two different distributions.
  In contrast to prior work, one of the strengths of our approach is that the
sanitization is performed in the same space as the original data by only
modifying the other attributes as little as possible and thus preserving the
interpretability of the sanitized data. As a consequence, once the sanitizer is
trained, it can be applied to new data, such as for instance, locally by an
individual on his profile before releasing it. Finally, experiments on a real
dataset demonstrate the effectiveness of the proposed approach as well as the
achievable trade-off between fairness and utility.
