Before revision:  Motivated by this, we study fair classification problems when the protected attributes in the data may be ``noisy''. In particular, we consider a noise model where any protected type may be flipped to another with some fixed probability. 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:  Motivated by this, recent works have studied the fair classification problem in which a binary protected attribute is "noisy" (the protected type is flipped 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 .