Identifying and Correcting Label Bias in Machine Learning

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained
on such datasets can inherit these biases. In this
paper, we provide a mathematical formulation of
how this bias can arise. We do so by assuming
the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who
intends to provide accurate labels but may have
biases against certain groups. Despite the fact that
we only observe the biased labels, we are able to
show that the bias may nevertheless be corrected
by re-weighting the data points without changing the labels. We show, with theoretical guarantees, that training on the re-weighted dataset
corresponds to training on the unobserved but unbiased labels, thus leading to an unbiased machine
learning classifier. Our procedure is fast and robust and can be used with virtually any learning
algorithm. We evaluate on a number of standard
machine learning fairness datasets and a variety
of fairness notions, finding that our method outperforms standard approaches in achieving fair
classification.

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