Reviews: Unlocking Fairness: a Trade-off Revisited

The premise of the paper is that many other papers in the ML fairness literature assume that there is an inevitable trade-off between fairness and accuracy, often without adequate justification for this assumption, and that many papers either assume that the data itself is unbiased or at least do not explicitly their assumptions about the types of bias in the data. I am not fully convinced that the paper's characterization of previous work is accurate. In my view, most fairness papers typically work in one of the following two regimes: 1. The setting in which the learner has access to fair, correct ground truth. In this case, there is clearly no trade-off between fairness and accuracy; if the training and test data are fair, a perfectly accurate classifier would also be perfectly fair.