不完美世界中的完美反事实：序列算法追索中动作噪声实现的建模
Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse
AI总结：
针对序列算法追索中多步骤累积噪声导致实施不完美的问题，提出基于马尔可夫决策过程的ROSE追索生成器，在保证稀疏性和计算效率的同时有效平衡鲁棒性与成本。
摘要：
算法追索为受到自动决策不利影响的个体建议行动，帮助他们实现期望的结果。然而，了解追索并不能保证用户能够完美地实施它，这可能是由于环境的多变性或个人选择所致。因此，追索生成应预见到其次优或带噪的实施。尽管有几种方法构建了对小扰动具有鲁棒性的追索——例如，由于其实施过程中的噪声引起的扰动——但它们假设整个追索是在单步内实施的，从而将噪声建模为一次性的且均匀的。但这些假设是不切实际的，因为追索通常需要多个序列步骤，这使得实施变得更加困难并受到不断增加的噪声影响。在这项工作中，我们考虑在遵循局部数据几何并在每一步累积的合理噪声下的追索。我们将此问题构建为马尔可夫决策过程，并证明这种合理噪声的分布满足马尔可夫性质。然后，我们针对表格数据提出了鲁棒序列（ROSE）追索生成器；我们的方法即使在实施不完美时也能产生一系列步骤，最终导向期望的结果。鉴于对次优人类行动的合理建模以及对累积不确定性的更强追索鲁棒性，ROSE在保持低追索成本的同时为用户提供了高成功率。实证评估表明，我们的算法在确保稀疏性和计算效率的同时，有效地平衡了追索鲁棒性与成本之间的内在权衡。
英文摘要：
Algorithmic recourse suggests actions to individuals who have been adversely affected by automated decision-making, helping them to achieve the desired outcome. Knowing the recourse, however, does not guarantee that users can implement it perfectly, either due to environmental variability or personal choices. Recourse generation should thus anticipate its sub-optimal or noisy implementation. While several approaches construct recourse that is robust to small perturbations -- e.g., arising due to its noisy implementation -- they assume that the entire recourse is implemented in a single step, thus model the noise as one-off and uniform. But these assumptions are unrealistic since recourse often entails multiple sequential steps, which makes it harder to implement and subject to increasing noise. In this work, we consider recourse under plausible noise that adheres to the local data geometry and accumulates at every step of the way. We frame this problem as a Markov Decision Process and demonstrate that such a distribution of plausible noise satisfies the Markov property. We then propose the RObust SEquential (ROSE) recourse generator for tabular data; our method produces a series of steps leading to the desired outcome even when they are implemented imperfectly. Given plausible modelling of sub-optimal human actions and greater recourse robustness to accumulated uncertainty, ROSE provides users with a high chance of success while maintaining low recourse cost. Empirical evaluation shows that our algorithm effectively navigates the inherent trade-off between recourse robustness and cost while ensuring its sparsity and computational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
