  With the increasing reliance on automated decision making, the issue of
algorithmic fairness has gained increasing importance. In this paper, we
propose a Generative Adversarial Network for tabular data generation. The model
includes two phases of training. In the first phase, the model is trained to
accurately generate synthetic data similar to the reference dataset. In the
second phase we modify the value function to add fairness constraint, and
continue training the network to generate data that is both accurate and fair.
We test our results in both cases of unconstrained, and constrained fair data
generation. In the unconstrained case, i.e. when the model is only trained in
the first phase and is only meant to generate accurate data following the same
joint probability distribution of the real data, the results show that the
model beats state-of-the-art GANs proposed in the literature to produce
synthetic tabular data. Also, in the constrained case in which the first phase
of training is followed by the second phase, we train the network and test it
on four datasets studied in the fairness literature and compare our results
with another state-of-the-art pre-processing method, and present the promising
results that it achieves. Comparing to other studies utilizing GANs for fair
data generation, our model is comparably more stable by using only one critic,
and also by avoiding major problems of original GAN model, such as
mode-dropping and non-convergence, by implementing a Wasserstein GAN.
