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.

本文回顾了以往提出的公正度量标准，并讨论了绩效基准框架以及替代度量标准的评估。

生成模型中的公平性测量

Measuring Fairness in Generative Models

Fairness-aware learning is increasingly important in data mining.
Discrimination prevention aims to prevent discrimination in the training data
before it is used to conduct predictive analysis. In this paper, we focus on
fair data generation that ensures the generated data is discrimination free.
Inspired by generative adversarial networks (GAN), we present fairness-aware
generative adversarial networks, called FairGAN, which are able to learn a
generator producing fair data and also preserving good data utility. Compared
with the naive fair data generation models, FairGAN further ensures the
classifiers which are trained on generated data can achieve fair classification
on real data. Experiments on a real dataset show the effectiveness of FairGAN.

本文提出了一种名为 FairGAN 的公平性生成对抗网络，旨在生成既不带偏见又具有较好数据实用性的数据。与基本的公平数据生成模型相比，FairGAN 还确保了对生成数据进行分类的分类器可以在真实数据上实现公平分类。