Reviews: Noise-tolerant fair classification

Section 1 describes the set-up of the problem. In particular, the authors emphasize that there are two cases where features might have noise in them: 1) when noise is deliberately added by researchers for privacy purposes and 2) in the "positive and unlabeled" setting where individual participants in the minority group might feel uncomfortable disclosing that, leading to unlabeled data for the sensitive feature in some cases. The case under consideration is binary classification on output Y with a binary sensitive feature A . There are two main assumptions in this paper. The first is that the noise can be described as "mutually contaminated learning".