As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas. This work proposes fairness penalties learned by neural networks with a simple random sampler of sensitive attributes for non-discriminatory supervised learning. In contrast to many existing works that critically rely on the discreteness of sensitive attributes and response variables, the proposed penalty is able to handle versatile formats of the sensitive attributes, so it is more extensively applicable in practice than many existing algorithms. This penalty enables us to build a computationally efficient group-level in-processing fairness-aware training framework. Empirical evidence shows that our framework enjoys better utility and fairness measures on popular benchmark data sets than competing methods. We also theoretically characterize estimation errors and loss of utility of the proposed neural-penalized risk minimization problem.

翻译：随着数据驱动的决策过程在工业应用中占据主导地位，公平感知机器学习在各个领域引起了广泛关注。本文提出了一种通过神经网络学习并结合敏感属性简单随机采样器的公平惩罚项，用于实现非歧视性的监督学习。与许多现有方法严重依赖敏感属性和响应变量的离散性不同，本文提出的惩罚项能够处理多种格式的敏感属性，因此在实践中比许多现有算法具有更广泛的适用性。该惩罚项使我们能够构建一个计算高效的群体层面处理中公平感知训练框架。实证结果表明，在流行的基准数据集上，我们的框架在效用和公平性指标上优于竞争方法。此外，我们从理论上刻画了所提出的神经惩罚风险最小化问题的估计误差和效用损失。