We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes. Compared to prior work, our framework can be employed with a very general class of linear and linear-fractional fairness constraints, can handle multiple, non-binary protected attributes, and outputs a classifier that comes with provable guarantees on both accuracy and fairness. Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise is large, in two real-world datasets.

翻译：我们提出了一个优化框架,在受保护属性出现噪音扰动的情况下学习公平分类。 与先前的工作相比,我们的框架可以用一个非常普通的线性和线性公平约束类别来使用,可以处理多个非二进制受保护属性,而产出则可以处理多个非二进制受保护属性,同时对准确性和公平性提供可辨证的保障。 简而言之,我们表明,我们的框架可以被用于实现统计率或虚假的正价公平保证,而准确性损失最小,即使噪音很大,在两个真实世界的数据集中也是如此。