算法补救方案随时间可能失效，本文提出考虑时间因素的改进方法。
Time Can Invalidate Algorithmic Recourse
- 基于因果视角，分析算法补救随时间变化的失效机制。
- 实验证明即使稳健的因果方法也会因世界动态而失效。
- 提出新算法显式建模时间，提升对数据趋势的适应性。
算法补救（AR）旨在为用户提供建议，以改变机器学习模型的不利决策。然而，这些建议通常需要时间实现（如获得学位需数年），且其效果可能随世界变化而改变。因此，我们应关注在动态环境中仍有效的补救方案。本文从因果角度研究算法补救的时间鲁棒性。理论与实证表明，即使稳健的因果AR方法也将在非静态世界中失效，除非世界完全平稳——这几乎不可能。更关键的是，除非世界完全确定，否则反事实AR无法最优求解。为此，我们提出一种简单有效的时序AR算法，在假设可获得随机过程估计器的前提下，显式建模时间。在合成与真实数据集上的模拟显示，考虑时间因素可产生更抗趋势的解决方案。
原文摘要 · Abstract (English)
Algorithmic Recourse (AR) aims to provide users with actionable steps to overturn unfavourable decisions made by machine learning predictors. However, these actions often take time to implement (e.g., getting a degree can take years), and their effects may vary as the world evolves. Thus, it is natural to ask for recourse that remains valid in a dynamic environment. In this paper, we study the robustness of algorithmic recourse over time by casting the problem through the lens of causality. We demonstrate theoretically and empirically that (even robust) causal AR methods can fail over time, except in the -- unlikely -- case that the world is stationary. Even more critically, unless the world is fully deterministic, counterfactual AR cannot be solved optimally. To account for this, we propose a simple yet effective algorithm for temporal AR that explicitly accounts for time under the assumption of having access to an estimator approximating the stochastic process. Our simulations on synthetic and realistic datasets show how considering time produces more resilient solutions to potential trends in the data distribution.
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