Predictive modelling is often reduced to finding the best model that optimizes a selected performance measure. But what if the second-best model describes the data in a completely different way? What about the third-best? Is it possible that the equally effective models describe different relationships in the data? Inspired by Anscombe's quartet, this paper introduces a Rashomon quartet, a four models built on synthetic dataset which have practically identical predictive performance. However, their visualization reveals distinct explanations of the relation between input variables and the target variable. The illustrative example aims to encourage the use of visualization to compare predictive models beyond their performance.

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.