Learning Fair and Interpretable Representations via Linear Orthogonalization
This addresses fairness issues in high-stakes decision-making for applications like hiring or lending, offering an interpretable and transferable solution, though it appears incremental as it builds on existing debiasing approaches.
The paper tackled the problem of algorithmic discrimination by proposing a geometric method to remove correlations between data and protected variables, resulting in more accurate and fair predictions compared to state-of-the-art fair AI algorithms across benchmark datasets.
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.