Weakness: 1. As mentioned in Remark 1, the KDE would requires more data when the classifier space is beyond binary. However for other approach, Zhang et. al.[44], this seems not a problem. I wonder whether it is possible to show the comparison of the performance on datasets that the decision space is beyond binary? 2. Is it possible to show the relation between the size of decision space and the amount of data required for KDE? 3. Experiments: a. My first concerns in the experiment section is whether the dataset is challenging enough? The setting of all real-world datasets seems very simplified. Specifically, the decision space is binary and only ONE attribute is considered as sensitive information. Therefore, I wonder whether the proposed approach would work in a more complex setting where more attributes are considered as sensitive information. b. I have another concerns about the potential impact of the proposed approach. Specifically, I wonder would this approach be generalized to other tasks? Comparing with the experiments in [44], the approach in [44] seems could be applied into a more broad use case, which is the de-biasing on word embedding. However, it seems non-trivial for the proposed approach to generalize beyond the binary decision problem. c. Comparing with [44], apart from stabilized training, what is the advantage of the proposed approach over [44]. Performance wise, it seems the proposed approach achieves similar results as [44] in Credit Card dataset and COMPAS dataset. Computational time wise, the proposed approach is actually slower than [44] on Law School dataset and adult census datasets. Application wise, it seems [44] could be applied to a broader application domain than the proposed approach.
1. As mentioned in Remark 1, the KDE would requires more data when the classifier space is beyond binary. However for other approach, Zhang et. al.[44], this seems not a problem. I wonder whether it is possible to show the comparison of the performance on datasets that the decision space is beyond binary?