  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 employed with a very general class of linear and
linear-fractional fairness constraints, can handle multiple, non-binary
protected attributes, and outputs a classifier that comes with provable
guarantees on both accuracy and fairness. 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, even when the noise is
large, in two real-world datasets.
