用可解释的决策树识别并减少算法歧视
FairUDT: Fairness-aware Uplift Decision Trees
- 将增益建模与决策树结合，引入公平性分裂标准
- 在三个基准数据集上实现准确率与偏见的合理平衡
- 适合需要透明决策的公平性检测场景
用于训练机器学习分类器的数据可能对特定受保护属性（如性别、宗教、种族）存在偏差。这些偏差通常源于历史歧视或导致少数群体代表性不足的潜在模式。本文提出一种新方法 FairUDT——一种面向公平性的增益决策树，用于识别歧视。FairUDT 展示了如何将增益建模与决策树结合，并引入公平性分裂准则。我们根据二值敏感属性将数据集分为有利组（处理组）和不利组（对照组），并通过改进的叶节点重标记策略消除歧视。在三个基准数据集上应用 FairUDT 及其叶节点重标记方法后，实现了可接受的准确率-偏见权衡。结果表明，FairUDT 本身具有内在可解释性，适用于歧视检测任务。项目代码已公开于 https://github.com/ara-25/FairUDT。
原文摘要 · Abstract (English)
Training data used for developing machine learning classifiers can exhibit biases against specific protected attributes. Such biases typically originate from historical discrimination or certain underlying patterns that disproportionately under-represent minority groups, such as those identified by their gender, religion, or race. In this paper, we propose a novel approach, FairUDT, a fairness-aware Uplift-based Decision Tree for discrimination identification. FairUDT demonstrates how the integration of uplift modeling with decision trees can be adapted to include fair splitting criteria. Additionally, we introduce a modified leaf relabeling approach for removing discrimination. We divide our dataset into favored and deprived groups based on a binary sensitive attribute, with the favored dataset serving as the treatment group and the deprived dataset as the control group. By applying FairUDT and our leaf relabeling approach to preprocess three benchmark datasets, we achieve an acceptable accuracy-discrimination tradeoff. We also show that FairUDT is inherently interpretable and can be utilized in discrimination detection tasks. The code for this project is available https://github.com/ara-25/FairUDT
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