In recent years, explainability in machine learning has gained importance. In this context, counterfactual explanation (CE), which is an explanation method that uses examples, has attracted attention. However, it has been pointed out that CE is not robust when there are multiple machine-learning models. These problems are important when using machine learning to make safe decisions. In this paper, we propose robust CEs that introduce a new viewpoint - Pareto improvement - and a method that uses multi-objective optimization to generate it. To evaluate the proposed method, we conducted experiments using both simulated and actual data. The results demonstrate that the proposed method is robust and useful. We believe that this research will contribute to a wide range of research areas, such as explainability in machine learning, decision-making, and action planning based on machine learning.

翻译：近年来，机器学习可解释性日益受到重视。在此背景下，反事实解释作为一种基于示例的解释方法备受关注。然而，有研究指出当存在多个机器学习模型时，反事实解释缺乏鲁棒性。这些在使用机器学习进行安全决策时尤为重要。本文提出一种引入新视角——帕累托改进——的鲁棒反事实解释方法，并采用多目标优化技术生成此类解释。为评估所提方法，我们通过模拟数据和实际数据进行了实验验证。结果表明，所提方法具有鲁棒性和实用性。我们相信这项研究将对机器学习可解释性、基于机器学习的决策制定与行动规划等多个研究领域产生广泛贡献。