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:解释算法决定和提出可采取行动的反馈对于机器学习应用越来越重要。最近,已经投入大量努力,寻找一套多种多样的求助手段,以涵盖用户的偏好。然而,现有的工作往往忽视了追索方法应当接近数据方的要求;因此,已建的追索方法可能不可信,对用户来说可能不满意。为了解决这些问题,我们建议了一种新颖的方法,明确指导对数据方方面面的一套不同的可采取行动的求助方法。我们首先在有利类别中找到一套不同的原型,以平衡多样性和近距离之间的权衡。我们展示了两种具体的方法来找到这些原型:要么找到对确定点进程的后遗估计,要么解决一个四边两边程序。为了确保可操作性限制,我们用一个可操作性图,将节点代表培训样品和边缘表明两个实例之间的可行行动。我们随后找到一个可行的路径,然后这条路径显示每个原型模式的可行行动方法。我们展示了计划中每一项追索的可行行动。我们实验结果显示两种具体的方法:要么是找到一种比现有数据更接近成本的多样化的方法。