Identifying and Correcting Label Bias in Machine Learning
This addresses fairness issues in ML for disadvantaged groups, offering a practical solution with broad applicability, though it builds on existing bias correction methods.
The paper tackles the problem of label bias in machine learning datasets by proposing a re-weighting method that corrects biases without altering labels, showing theoretical guarantees for training on unbiased labels and outperforming standard fairness approaches on multiple datasets.
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.