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 be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy for significant noise levels .