Before revision:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
After revision:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy , even when the noise corruption is large in two real-world datasets. Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .