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.

研究论文介绍了一种名为公平 MP-Boost 的随机增强方案，旨在在保持预测能力的基础上提高公平性和可解释性。通过采用自适应学习特征和观测的方式，该方法平衡了准确性和公平性，并通过最相关的全局学习子集选择重要和公平特征来提高模型的性能。经过实证评估，展示了公平 MP-Boost 在模拟和基准数据集上的解释性、准确性和公平性。