  There are several algorithms for measuring fairness of ML models. A
fundamental assumption in these approaches is that the ground truth is fair or
unbiased. In real-world datasets, however, the ground truth often contains data
that is a result of historical and societal biases and discrimination. Models
trained on these datasets will inherit and propagate the biases to the model
outputs. We propose FAIRLABEL, an algorithm which detects and corrects biases
in labels. The goal of FAIRLABELis to reduce the Disparate Impact (DI) across
groups while maintaining high accuracy in predictions. We propose metrics to
measure the quality of bias correction and validate FAIRLABEL on synthetic
datasets and show that the label correction is correct 86.7% of the time vs.
71.9% for a baseline model. We also apply FAIRLABEL on benchmark datasets such
as UCI Adult, German Credit Risk, and Compas datasets and show that the
Disparate Impact Ratio increases by as much as 54.2%.
