text: The proposed method is a classifier that is fair and works in collaboration with an unfair (but presumably accurate model).  The novel classifier is the result of the optimisation of a loss function  (composed of a part similar to a logistic regression model and a part being the disparate impact).  Hence, it can be interpreted as a logistic loss with a fairness regularisation. The results are promising and the applications are very important for the acceptance of ML approaches in the society.  However, I believe that the model could be made more general (than a fairness regularized logistic loss) and its theoretical properties studied. Finally, this paper used uncommon vocabulary (for the machine learning community)  and it make is difficult to follow sometimes (for example, the use of a Decision-Maker entity). When reading the submitted paper, it was unclear (until section 6) how deferring could help fairness.  Hence, the structure of the paper could maybe be improved by introducing the cost function earlier in the manuscript (as a fairness regularised loss). To conclude, although the application is of high interest and the numerical results encouraging,  the methodological approach does not seem to be very novel. Minor comment : - The list of authors of the reference “Machine bias : theres software…” apperars incorrectly (some comma may be missing in the .bib file)  and there is a small typo in the title. Possible extensions :- The proposed fairness aware loss could be made more general (and not only in the case of a logistic model)  - It could also be generalised to a mixture of biased classifier (more than on DM).