Before revision:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with  linear and linear-fractional class of fairness constraints,
After revision:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes . Compared to prior work, our framework can be employed with linear and linear-fractional class of fairness constraints,