The Wasserstein GAN (WGAN) is a well-established model allowing for the generation of high-quality synthetic data approximating a given real dataset. We study TabFairGAN, a known tabular variation of WGAN in which a custom penalty term is added to the generator's loss, forcing it to produce fair data. Here we measure the fairness of synthetic data using demographic parity, i.e., the gap in the proportions of positive outcome between different sensitive groups. We reproduce some results from the…