Algorithmic recourse recommends a cost-efficient action to a subject to
reverse an unfavorable machine learning classification decision. Most existing
methods in the literature generate recourse under the assumption of complete
knowledge about the cost function. In real-world practice, subjects could have
distinct preferences, leading to incomplete information about the underlying
cost function of the subject. This paper proposes a two-step approach
integrating preference learning into the recourse generation problem. In the
first step, we design a question-answering framework to refine the confidence
set of the Mahalanobis matrix cost of the subject sequentially. Then, we
generate recourse by utilizing two methods: gradient-based and graph-based
cost-adaptive recourse that ensures validity while considering the whole
confidence set of the cost matrix. The numerical evaluation demonstrates the
benefits of our approach over state-of-the-art baselines in delivering
cost-efficient recourse recommendations.

该论文提出了一种将偏好学习整合到追索生成问题中的两步方法，通过设计问答框架来逐步细化追索主体的马氏矩阵代价的置信集，然后利用基于梯度和基于图的代价自适应追索方法来生成合法且考虑到整个代价矩阵的高效追索，数值评估结果表明该方法在提供高效追索建议方面优于现有基准。