Before revision:  non-binary protected attributes  . 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 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 .
After revision:  non-binary protected attributes . 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 . 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 .