Fairness is essential for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, deriving from a consensus that `similar individuals should be treated similarly,' is a vital notion to describe fair treatment for individual cases. Previous studies typically characterize individual fairness as a prediction-invariant problem when perturbing sensitive attributes on samples, and solve it by Distributionally Robust Optimization (DRO) paradigm. However, such adversarial perturbations along a direction covering sensitive information used in DRO do not consider the inherent feature correlations or innate data constraints, therefore could mislead the model to optimize at off-manifold and unrealistic samples. In light of this drawback, in this paper, we propose to learn and generate antidote data that approximately follows the data distribution to remedy individual unfairness. These generated on-manifold antidote data can be used through a generic optimization procedure along with original training data, resulting in a pure pre-processing approach to individual unfairness, or can also fit well with the in-processing DRO paradigm. Through extensive experiments on multiple tabular datasets, we demonstrate our method resists individual unfairness at a minimal or zero cost to predictive utility compared to baselines.

翻译：公平性对于在高镜头应用中部署的机器学习系统至关重要。在所有公平概念中,个人公平性是描述个人案例公平待遇的一个重要概念。 以往的研究通常将个人公平性定性为在扰乱样本敏感属性时的预测-变化问题,并通过分布式强力优化优化模式加以解决。然而,在涉及DRO使用的敏感信息的方向上,这种对抗性干扰并不考虑内在特征的关联性或内在数据限制,因此有可能误导模型优化非自制和不切实际的样本。鉴于这一缺陷,我们提议学习和生成解毒数据,大致遵循数据分配,以纠正个人不公现象。这些自制解毒数据可以通过一种通用优化程序与原始培训数据一起使用,从而导致纯粹的处理前处理方法处理个人不公,或与处理中DRO模式相匹配。通过对多个表格数据集的广泛实验,我们证明我们的方法在最低或最低水平的基线比零基线上抵制个人不公用性。</s>