Individually Fair Gradient Boosting

Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun

Keywords: Algorithmic fairness, boosting, non-smooth models

Abstract: We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias.

One-sentence Summary: We propose an algorithm for training individually fair gradient boosted decision trees classifiers.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics