用未来模型预测降低算法救济成本，兼顾准确与鲁棒性
Learning-Augmented Robust Algorithmic Recourse
- 结合未来模型预测优化救济方案，减少调整代价
- 预测准确时成本显著降低，预测错误时仍保持可控代价
- 适合关注公平性与系统稳定性的算法设计者
算法救济为机器学习系统中获得不良结果的个体提供最小成本的改进路径以获得理想结果。然而，模型更新常导致原有救济方案失效。现有鲁棒救济框架虽能抵抗模型扰动，但代价较高。本文首次研究学习增强型算法救济，评估设计者若能预判未来模型，可在预测准确时降低救济成本（一致性），同时在预测不准时仍控制成本（鲁棒性）。提出新算法，分析鲁棒性与一致性之间的权衡，揭示预测准确率对性能的影响。
原文摘要 · Abstract (English)
Algorithmic recourse provides individuals who receive undesirable outcomes from machine learning systems with minimum-cost improvements to achieve a desirable outcome. However, machine learning models often get updated, so the recourse may not lead to the desired outcome. The robust recourse framework chooses recourses that are less sensitive to adversarial model changes, but this comes at a higher cost. To address this, we initiate the study of learning-augmented algorithmic recourse and evaluate the extent to which a designer equipped with a prediction of the future model can reduce the cost of recourse when the prediction is accurate (consistency) while also limiting the cost even when the prediction is inaccurate (robustness). We propose a novel algorithm, study the robustness-consistency trade-off, and analyze how prediction accuracy affects performance.
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