Review for NeurIPS paper: A Fair Classifier Using Kernel Density Estimation

The paper proposes a simple but rather practical approach to estimate statistical fairness notions without relying on a proxy, in contrast to several prior work. The proposed approach relies on Kernel Density Estimation (KDE), which allows to compute the gradient of the fairness notion with respect to the model parameters in close form, easing the learning procedure of a fair classifier. As a result, he proposed approach leads to a better fairness accuracy trade-off than competing methods in several datasets. In particular, the experiments show that the proposed approach outperforms prior work relying on fairness proxies, and leads more stable results that approaches that rely on adversarial training top trade-off fairness and accuracy. In fact, the empirical results are comparable to the ones provided by Agarwal et al. (2018), whose solution provide theoretical guarantees but comes at a high computational cost. Although there exists extensive literature on solving the fair classification problem, the empirical results show the efficacy of KDE in this context.