论文标题
具有嘈杂保护属性的公平分类：具有可证明保证的框架
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
论文作者
论文摘要
我们提出了一个优化框架，用于在受保护属性中存在嘈杂的扰动时学习公平的分类器。与先前的工作相比，我们的框架可以采用非常一般的线性和线性分数公平性约束，可以处理多个，非二进制保护的属性，并输出一个分类器，该分类器在准确性和公平性方面具有可证明的保证。从经验上讲，我们表明我们的框架可用于在两个现实世界中的数据集中，即使噪声很大，准确性损失也最小，即使噪声很大，也可以用来达到统计率或假正率公平性。
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.
