  To reduce human error and prejudice, many high-stakes decisions have been
turned over to machine algorithms. However, recent research suggests that this
does not remove discrimination, and can perpetuate harmful stereotypes. While
algorithms have been developed to improve fairness, they typically face at
least one of three shortcomings: they are not interpretable, their prediction
quality deteriorates quickly compared to unbiased equivalents, and they are not
easily transferable across models. To address these shortcomings, we propose a
geometric method that removes correlations between data and any number of
protected variables. Further, we can control the strength of debiasing through
an adjustable parameter to address the trade-off between prediction quality and
fairness. The resulting features are interpretable and can be used with many
popular models, such as linear regression, random forest, and multilayer
perceptrons. The resulting predictions are found to be more accurate and fair
compared to several state-of-the-art fair AI algorithms across a variety of
benchmark datasets. Our work shows that debiasing data is a simple and
effective solution toward improving fairness.
