  Deep generative models have made much progress in improving training
stability and quality of generated data. Recently there has been increased
interest in the fairness of deep-generated data. Fairness is important in many
applications, e.g. law enforcement, as biases will affect efficacy. Central to
fair data generation are the fairness metrics for the assessment and evaluation
of different generative models. In this paper, we first review fairness metrics
proposed in previous works and highlight potential weaknesses. We then discuss
a performance benchmark framework along with the assessment of alternative
metrics.
