用双目标优化让决策树更公平，不改数据也能降偏。
Software Engineering Principles for Fairer Systems: Experiments with GroupCART
- 同时优化预测准确和群体属性熵，防止模型偏见
- 在不改数据情况下提升公平性，性能损失极小
- 支持灵活权衡准确率与公平性，适合实际部署
歧视感知分类旨在实现准确预测的同时满足公平性约束。传统决策树学习器仅优化目标属性的信息增益，可能导致对性别、族裔等受保护群体的不公平。为此，我们提出GroupCART，一种基于决策树的集成优化器，在模型构建过程中通过同时最小化目标属性熵、最大化受保护属性熵来避免偏差。实验表明，GroupCART在无需数据变换的情况下实现了更公平的模型，且性能下降微小。该方法支持自定义权重，可根据用户需求平滑调节预测性能与公平性的权衡。结果证明，通过多任务、公平感知学习可有效缓解决策树模型中的算法偏见。本研究所有代码与数据集均开源：https://github.com/anonymous12138/groupCART。
原文摘要 · Abstract (English)
Discrimination-aware classification aims to make accurate predictions while satisfying fairness constraints. Traditional decision tree learners typically optimize for information gain in the target attribute alone, which can result in models that unfairly discriminate against protected social groups (e.g., gender, ethnicity). Motivated by these shortcomings, we propose GroupCART, a tree-based ensemble optimizer that avoids bias during model construction by optimizing not only for decreased entropy in the target attribute but also for increased entropy in protected attributes. Our experiments show that GroupCART achieves fairer models without data transformation and with minimal performance degradation. Furthermore, the method supports customizable weighting, offering a smooth and flexible trade-off between predictive performance and fairness based on user requirements. These results demonstrate that algorithmic bias in decision tree models can be mitigated through multi-task, fairness-aware learning. All code and datasets used in this study are available at: https://github.com/anonymous12138/groupCART.
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