Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates under the assumption that the sensitive feature available in one's training sample is perfectly reliable. This assumption may be violated in many real-world cases: for example, respondents to a survey may choose to conceal or obfuscate their group identity out of fear of potential discrimination. This poses the question of whether one can still learn fair classifiers given noisy sensitive features. In this paper, we answer the question in the affirmative: we show that if one measures fairness using the mean-difference score, and sensitive features are subject to noise from the mutually contaminated learning model, then owing to a simple identity we only need to change the desired fairness-tolerance. The requisite tolerance can be estimated by leveraging existing noise-rate estimators from the label noise literature. We finally show that our procedure is empirically effective on two case-studies involving sensitive feature censoring.

中文翻译：

---

耐噪公平分类

公平意识学习涉及设计不区分某些敏感特征（例如种族或性别）的算法。该问题的现有工作是在假设一个人的训练样本中可用的敏感特征完全可靠的情况下进行的。在许多现实世界的案例中，这一假设可能会被违反：例如，调查的受访者可能会因为害怕潜在的歧视而选择隐藏或混淆他们的群体身份。这提出了一个问题，即考虑到嘈杂的敏感特征，人们是否仍然可以学习公平的分类器。在本文中，我们肯定地回答了这个问题：我们表明，如果使用均值差分数来衡量公平性，并且敏感特征会受到来自相互污染的学习模型的噪声的影响，那么由于一个简单的身份，我们只需要改变所需的公平容忍度。可以通过利用标签噪声文献中现有的噪声率估计器来估计必要的容差。我们最终表明，我们的程序在涉及敏感特征审查的两个案例研究中在经验上是有效的。