This paper proposes a framework which can learn classifiers that satisfy multiple notions of fairness such as fairness through unawareness, demographic parity, equalized odds etc. The proposed framework leverages ideas from two different lines of existing research namely, distribution matching and privileged learning, in order to accommodate multiple notions of fairness. This work builds on two prior papers on fairness - Hardt et. The proposed method seems interesting and novel, and the ideas from privileged learning and distribution matching have not been employed in designing fair classifiers so far. The idea of proposing a generalized framework which can handle multiple notions of fairness is quite appealing. The paper, however, has the following weaknesses: 1) the evaluation is weak; the baselines used in the paper are not even designed for fair classification 2) the optimization procedure used to solve the multi-objective optimization problem is not discussed in adequate detail Detailed comments below: Methods and Evaluation: The proposed objective is interesting and utilizes ideas from two well studied lines of research, namely, privileged learning and distribution matching to build classifiers that can incorporate multiple notions of fairness.