提出局部统计平等准则，实现公平决策树的高效构建。
Local Statistical Parity for the Estimation of Fair Decision Trees
- 在节点层面引入局部统计平等，与全局公平性一致
- 新算法C-LRT在保持准确率的同时显著提升公平性
- 适合需要平衡精度与公平性的实际决策系统
由于决策树估计具有高计算复杂度，传统方法采用递归方式逐节点构建。为促进公平性，本文提出一种基于树节点的局部公平性准则，证明其与算法公平领域常用的统计平等准则相关，并可融入标准递归树构建算法。提出一种名为约束逻辑回归树（C-LRT）的新算法，它是对标准CART算法的改进，采用局部线性分类器并借鉴约束逻辑回归的限制机制。在算法公平文献中常用的数据集上，使用多种分类与公平性指标评估了C-LRT估计的树性能。结果表明，C-LRT能有效控制并平衡准确性与公平性。
原文摘要 · Abstract (English)
Given the high computational complexity of decision tree estimation, classical methods construct a tree by adding one node at a time in a recursive way. To facilitate promoting fairness, we propose a fairness criterion local to the tree nodes. We prove how it is related to the Statistical Parity criterion, popular in the Algorithmic Fairness literature, and show how to incorporate it into standard recursive tree estimation algorithms. We present a tree estimation algorithm called Constrained Logistic Regression Tree (C-LRT), which is a modification of the standard CART algorithm using locally linear classifiers and imposing restrictions as done in Constrained Logistic Regression. Finally, we evaluate the performance of trees estimated with C-LRT on datasets commonly used in the Algorithmic Fairness literature, using various classification and fairness metrics. The results confirm that C-LRT successfully allows to control and balance accuracy and fairness.
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