Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses the so-called counterfactual paths generated by conditional permutations of features. The algorithm measures feature importance by identifying sequential permutations of features that most influence changes in model predictions. It is particularly suitable for generating explanations based on counterfactual paths in knowledge graphs incorporating domain knowledge. Counterfactual paths introduce an additional graph dimension to current XAI methods in both explaining and visualizing black-box models. Experiments with synthetic and medical data demonstrate the practical applicability of our approach.

翻译：可解释人工智能（XAI）是机器学习研究中日益重要的领域，旨在使黑箱模型透明且可解释。本文提出一种新颖的XAI方法，利用特征条件排列生成的反事实路径。该算法通过识别对模型预测变化影响最大的特征顺序排列来度量特征重要性，特别适用于结合领域知识的知识图谱中基于反事实路径的生成解释。反事实路径为当前XAI方法在解释与可视化黑箱模型方面引入了额外的图维度。基于合成数据与医学数据的实验验证了本方法的实际适用性。