Intersectional Divergence: Measuring Fairness in Regression
Published in arXiv preprint, 2025
In this paper, we propose Intersectional Divergence (ID) as the first fairness measure for regression tasks that 1) describes fair model behavior across multiple protected attributes and 2) differentiates the impact of predictions in target ranges most relevant to users. We extend our proposal demonstrating how ID can be adapted into a loss function, IDLoss, and used in optimization problems. Through an extensive experimental evaluation, we demonstrate how ID allows unique insights into model behavior and fairness, and how incorporating IDLoss into optimization can considerably improve single-attribute and intersectional model fairness while maintaining a competitive balance in predictive performance.
Recommended citation: Germino, Joe, Nuno Moniz, and Nitesh V. Chawla. "Intersectional Divergence: Measuring Fairness in Regression." arXiv preprint arXiv:2505.00830 (2025). https://arxiv.org/abs/2505.00830