Fair MP-BOOST: Fair and Interpretable Minipatch Boosting

no code implementations • 1 Apr 2024 • Camille Olivia Little, Genevera I. Allen

Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data.

Fairness Feature Importance

Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates

no code implementations • 6 Oct 2023 • Camille Olivia Little, Debolina Halder Lina, Genevera I. Allen

Specifically, we develop a novel fair feature importance score for trees that can be used to interpret how each feature contributes to fairness or bias in trees, tree-based ensembles, or tree-based surrogates of any complex ML system.

Fairness Feature Importance +1

To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier

no code implementations • 31 May 2022 • Camille Olivia Little, Michael Weylandt, Genevera I Allen

Specifically, we identify and outline the empirical Pareto frontier through Tradeoff-between-Fairness-and-Accuracy (TAF) Curves; we then develop a metric to quantify this Pareto frontier through the weighted area under the TAF Curve which we term the Fairness-Area-Under-the-Curve (FAUC).

Fairness