  This paper evaluates synthetically generated healthcare data for biases and
investigates the effect of fairness mitigation techniques on utility-fairness.
Privacy laws limit access to health data such as Electronic Medical Records
(EMRs) to preserve patient privacy. Albeit essential, these laws hinder
research reproducibility. Synthetic data is a viable solution that can enable
access to data similar to real healthcare data without privacy risks.
Healthcare datasets may have biases in which certain protected groups might
experience worse outcomes than others. With the real data having biases, the
fairness of synthetically generated health data comes into question. In this
paper, we evaluate the fairness of models generated on two healthcare datasets
for gender and race biases. We generate synthetic versions of the dataset using
a Generative Adversarial Network called HealthGAN, and compare the real and
synthetic model's balanced accuracy and fairness scores. We find that synthetic
data has different fairness properties compared to real data and fairness
mitigation techniques perform differently, highlighting that synthetic data is
not bias free.
