Individually Fair Gradient Boosting
This addresses algorithmic fairness concerns in applications using gradient boosting, particularly for non-smooth models like decision trees, but is incremental as it builds on prior fairness methods.
The paper tackles the problem of enforcing individual fairness in gradient boosting, a popular method for tabular data, by proposing a functional gradient descent on a robust loss function; it demonstrates global convergence, generalization, and efficacy on three bias-susceptible ML problems.
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.