论文标题
计算合理的反事实解释的凸密度约束
Convex Density Constraints for Computing Plausible Counterfactual Explanations
论文作者
论文摘要
机器学习的越来越多以及诸如欧盟GDPR之类的法律法规的部署导致需要对机器学习模型提出的决策的用户友好解释。反事实解释被认为是解释模型特定决定的最流行技术之一。尽管对“任意”反事实解释的计算进行了充分的研究，但它仍然是一个开放的研究问题，如何有效地计算出合理且可行的反事实解释。我们基于最近的工作，并提出和研究对合理反事实解释的形式定义。特别是，我们研究了如何使用密度估计器来实现反事实解释的合理性和可行性。为了进行有效的计算，我们提出了凸密度约束，以确保所得的反事实位于高密度数据空间的区域。
The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models. Counterfactual explanations are considered as one of the most popular techniques to explain a specific decision of a model. While the computation of "arbitrary" counterfactual explanations is well studied, it is still an open research problem how to efficiently compute plausible and feasible counterfactual explanations. We build upon recent work and propose and study a formal definition of plausible counterfactual explanations. In particular, we investigate how to use density estimators for enforcing plausibility and feasibility of counterfactual explanations. For the purpose of efficient computations, we propose convex density constraints that ensure that the resulting counterfactual is located in a region of the data space of high density.
