基于易处理追索分布的多样化且合理的算法追索
Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions
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中文总结 AI 辅助
提出易处理追索分布概率框架，实现算法追索的多样性、合理性与可行性，在基准数据集和 MNIST 上验证了其有效性。
中文摘要 AI 辅助
算法追索旨在通过推荐可操作的改变来帮助个人逆转不利的自动化决策，从而获得期望的结果。由于个人通常有多种不同的途径来获得有利的决策，且不同的人可以采用不同的途径，因此追索系统应提供多种现实可行的替代方案，而非仅提供一种。现有方法将追索问题表述为优化问题，用于构建一个或少量反事实，而非对可行解的潜在空间进行建模，在实践中，这些方法往往为了确保其他方面而牺牲多样性、合理性或可行性。我们提出了易处理追索分布（Tractable Recourse Distributions），这是一种概率框架，用于将给定事实实例的可行替代方案空间表示为有利结果上的概率分布。对于基于 proximity（ proximity 此处保留英文，指特征接近度）和特征变化数量的常用代价函数，我们证明该分布可通过指数倾斜电路获得的概率电路进行精确表示；因此，每个个人的分布都以闭式形式存在，无需重新训练模型。从这些分布中采样自然会产生多样化且合理的追索方案，而倾斜参数则提供了对其接近度和稀疏性的显式控制。在标准算法追索基准数据集上的实验表明，所提出的框架同时实现了多样性、合理性和可行性，同时在可行反事实上保留了足够的概率质量，以确保拒绝采样切实可行。对 MNIST 的可视化研究说明了倾斜强度如何权衡接近度与有效性。
英文摘要
Algorithmic recourse seeks to help individuals reverse unfavorable automated decisions by recommending actionable changes that achieve a desired outcome. As an individual usually has several distinct routes to a favorable decision, and different people can act on different ones, a recourse system should offer multiple realistic alternatives rather than one. Existing approaches formulate recourse as an optimization problem that constructs one or a small set of counterfactuals rather than modeling the underlying space of feasible solutions, and in practice each sacrifices diversity, plausibility, or feasibility to secure the others. We propose Tractable Recourse Distributions, a probabilistic framework that represents the space of feasible alternatives for a given factual instance as a probability distribution over favorable outcomes. For commonly used cost functions based on proximity and the number of feature changes, we show that this distribution admits an exact representation as a probabilistic circuit, obtained by exponentially tilting the circuit; each individual's distribution is therefore available in closed form, without retraining the model. Sampling from these distributions naturally produces diverse and plausible recourses, while the tilting parameters provide explicit control over their proximity and sparsity. Experiments on standard algorithmic recourse benchmark datasets demonstrate that the proposed framework attains diversity, plausibility, and feasibility simultaneously, while retaining sufficient probability mass over feasible counterfactuals for rejection sampling to be practical. A visual study on MNIST illustrates how the tilt strength trades proximity against validity.
