Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees

L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1

We present 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…