Explaining algorithmic decisions and recommending actionable feedback is increasingly important for machine learning applications. Recently, significant efforts have been invested in finding a diverse set of recourses to cover the wide spectrum of users' preferences. However, existing works often neglect the requirement that the recourses should be close to the data manifold; hence, the constructed recourses might be implausible and unsatisfying to users. To address these issues, we propose a novel approach that explicitly directs the diverse set of actionable recourses towards the data manifold. We first find a diverse set of prototypes in the favorable class that balances the trade-off between diversity and proximity. We demonstrate two specific methods to find these prototypes: either by finding the maximum a posteriori estimate of a determinantal point process or by solving a quadratic binary program. To ensure the actionability constraints, we construct an actionability graph in which the nodes represent the training samples and the edges indicate the feasible action between two instances. We then find a feasible path to each prototype, and this path demonstrates the feasible actions for each recourse in the plan. The experimental results show that our method produces a set of recourses that are close to the data manifold while delivering a better cost-diversity trade-off than existing approaches.

翻译：解释算法决策并推荐可操作反馈对于机器学习应用日益重要。近年来，大量研究致力于寻找多样化的补救措施以覆盖用户偏好的广泛光谱。然而，现有工作往往忽视补救措施应接近数据流形的要求，因此构建的补救措施可能对用户而言不切实际且令人不满。为解决这些问题，我们提出了一种新方法，明确引导多样化的可操作补救措施朝向数据流形。首先，我们在有利类别中寻找一组平衡多样性与邻近性权衡的多样化原型。我们展示了两种具体方法来寻找这些原型：通过寻找行列式点过程的最大后验估计，或通过求解二次二进制规划。为确保可操作性约束，我们构建了一个可操作图，其中节点代表训练样本，边表示两个实例之间的可行操作。然后，我们找到通往每个原型的可行路径，该路径展示了计划中每个补救措施的可行操作。实验结果表明，我们的方法生成了一组接近数据流形的补救措施，同时实现了比现有方法更优的成本-多样性权衡。