With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination. In this paper, we focus on the pre-processing aspect for achieving fairness, and propose a data reweighing approach that only adjusts the weight for samples in the training phase. Different from most previous reweighing methods which assign a uniform weight for each (sub)group, we granularly model the influence from each training sample with regard to fairness and predictive utility, and compute individual weights based on the influence with constraints of both fairness and utility. Experimental results reveal that previous methods achieve fairness at a non-negligible cost of utility, while as a significant advantage, our approach can empirically release the tradeoff and obtain cost-free fairness. We demonstrate the cost-free fairness through vanilla classifiers and standard training processes on different fairness notions, compared to baseline methods on multiple tabular datasets.

本研究提出了一种数据重新加权方法，旨在通过调整训练样本的权重来实现公平性并保持预测效用，在多个真实世界数据集上对比基准方法，我们的方法可以在不影响效用的情况下，实现成本免费的公平性。

通过数据重新加权与影响实现无效益成本的公平性