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