Machine Learning's proliferation in critical fields such as healthcare, banking, and criminal justice has motivated the creation of tools which ensure trust and transparency in ML models. One such tool is Actionable Recourse (AR) for negatively impacted users. AR describes recommendations of cost-efficient changes to a user's actionable features to help them obtain favorable outcomes. Existing approaches for providing recourse optimize for properties such as proximity, sparsity, validity, and distance-based costs. However, an often-overlooked but crucial requirement for actionability is a consideration of User Preference to guide the recourse generation process. In this work, we attempt to capture user preferences via soft constraints in three simple forms: i) scoring continuous features, ii) bounding feature values and iii) ranking categorical features. Finally, we propose a gradient-based approach to identify User Preferred Actionable Recourse (UP-AR). We carried out extensive experiments to verify the effectiveness of our approach.

翻译：机器学习在医疗、金融和刑事司法等关键领域的广泛应用，催生了确保ML模型可信度与透明度的工具。可操作补救（Actionable Recourse, AR）便是针对受负面影响的用户设计的工具之一。AR通过推荐用户可操作特征的成本效益变更方案，帮助其获得有利结果。现有补救方法通常以邻近性、稀疏性、有效性及基于距离的成本等属性为优化目标。然而，一个常被忽视却至关重要的可操作性需求是：在生成补救方案时需关注用户偏好（User Preference）以引导过程。本研究尝试通过三种简单形式的软约束来捕捉用户偏好：i) 对连续特征进行评分，ii) 限定特征值范围，以及iii) 对分类特征排序。最终，我们提出一种基于梯度的方法来识别用户偏好的可操作补救（UP-AR），并通过大量实验验证了该方法的有效性。