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.

本文研究了含有噪声敏感特征的公平机器学习算法，表明当使用均值差异分数作为公平性量度标准时，只需通过调整所需的公平容错率即可仍然学习出公平分类器，该容错率可以通过现有噪声率估计器进行估计，并在两个敏感特征审查案例研究中得到实证有效性。