  Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative
Adversarial Networks (GANs) to impose fairness on the generated data, freeing
them from both disparate impact and disparate treatment. Given the model's
advantages and performance, we introduce a novel learning framework to transfer
a pre-trained FairGAN to other tasks. This reprogramming process has the goal
of maintaining the FairGAN's main targets of data utility, classification
utility, and data fairness, while widening its applicability and ease of use.
In this paper we present the technical extensions required to adapt the
original architecture to this new framework (and in particular the use of
Variational Auto-Encoders), and discuss the benefits, trade-offs, and
limitations of the new model.
