用双目标决策树生成更优的可操作建议摘要，兼顾效果与成本。
Optimal Recourse Summaries via Bi-Objective Decision Tree Learning
- 将建议摘要建模为双目标决策树，平衡效果与成本
- 在多个数据集上同时优于现有方法的效果与成本表现
- 可直接选择不同权衡方案，无需重新训练
可操作建议为个体提供改变不利分类结果的具体行动。虽然在实例层面有用，但难以用于全局审计与偏见检测，因为聚合局部建议成本高且不一致。建议摘要通过将人群分组并为每组分配一个共享行动，实现跨组比较。设计摘要面临根本性权衡：提升建议有效性往往导致成本上升，现有方法未能妥善处理此问题。我们提出最优全局可操作建议摘要（SOGAR），将摘要学习建模为最优决策树问题，并找到帕累托前沿——即在改进任一目标时必然损害另一目标的所有解。SOGAR支持事后选择理想权衡，无需重新训练。采用浅层轴平行决策树和稀疏叶节点行动，SOGAR生成稳定、低成本且有效的建议摘要，在多个有效性与成本指标上均超越现有方法。
原文摘要 · Abstract (English)
Actionable Recourse provides individuals with actions they can take to change an unfavorable classifier outcome. While useful at the instance level, it is ill-suited for global auditing and bias detection, since aggregating local actions is costly and often inconsistent. Recourse Summaries address this limitation by partitioning the population and assigning one shared action per subgroup, enabling comparison across subgroups. Designing summaries involves a fundamental trade-off between recourse effectiveness and recourse cost, which existing methods do not adequately address. We introduce Summaries of Optimal and Global Actionable Recourse (SOGAR), which formulates recourse summary learning as an optimal decision tree learning problem and finds the Pareto front -- the complete set of solutions where improving one objective necessarily worsens the other. SOGAR enables post-hoc selection of the desired trade-off without retraining. Using shallow axis-parallel decision trees and sparse leaf actions, SOGAR produces stable, low-cost, and effective recourse summaries that outperform existing approaches across effectiveness and cost metrics.
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