Fair MP-BOOST: Fair and Interpretable Minipatch Boosting
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. In this paper, we aim to leverage the robust predictive power of
traditional boosting methods while enhancing fairness and interpretability. To
achieve this, we develop Fair MP-Boost, a stochastic boosting scheme that
balances fairness and accuracy by adaptively learning features and observations
during training. Specifically, Fair MP-Boost sequentially samples small subsets
of observations and features, termed minipatches (MP), according to adaptively
learned feature and observation sampling probabilities. We devise these
probabilities by combining loss functions, or by combining feature importance
scores to address accuracy and fairness simultaneously. Hence, Fair MP-Boost
prioritizes important and fair features along with challenging instances, to
select the most relevant minipatches for learning. The learned probability
distributions also yield intrinsic interpretations of feature importance and
important observations in Fair MP-Boost. Through empirical evaluation of
simulated and benchmark datasets, we showcase the interpretability, accuracy,
and fairness of Fair MP-Boost.