Individually Fair Gradient Boosting

TL;DR

This paper introduces a novel method for enforcing individual fairness in gradient boosting models, capable of handling both smooth and non-smooth models like decision trees, with proven convergence and generalization.

Contribution

It proposes a new functional gradient descent approach on a robust loss function that encodes fairness, applicable to non-smooth models unlike prior methods.

Findings

Algorithm converges globally.

Method generalizes well.

Effective on bias-prone ML problems.

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.