Explainable AI (XAI) is an increasingly important area of research in machine learning, which in principle aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses counterfactual paths generated by conditional permutations. Our method provides counterfactual explanations by identifying alternative paths that could have led to different outcomes. The proposed method is particularly suitable for generating explanations based on counterfactual paths in knowledge graphs. By examining hypothetical changes to the input data in the knowledge graph, we can systematically validate the behaviour of the model and examine the features or combination of features that are most important to the model's predictions. Our approach provides a more intuitive and interpretable explanation for the model's behaviour than traditional feature weighting methods and can help identify and mitigate biases in the model.

翻译：可解释人工智能（XAI）是机器学习领域中日益重要的研究方向，其核心目标在于使黑箱模型具备透明性与可解释性。本文提出了一种基于条件排列生成反事实路径的新型XAI方法。该方法通过识别可能导致不同结果的替代路径来提供反事实解释，尤其适用于知识图谱中的反事实路径生成。通过系统性地验证输入数据的假设性变更，我们能够评估模型行为并分析对模型预测最关键的特征或特征组合。相较于传统特征加权方法，本方法为模型行为提供了更直观、更可解释的说明，同时有助于识别并缓解模型中的偏差。