Explaining and visualizing black-box models through counterfactual paths

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.

Explainable AI with counterfactual paths

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.

Performance is not enough: a story of the Rashomon's quartet

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 equally well but in a completely different way? What about the third? Is it possible that the most effective models learn completely different relationships in the data? Inspired by Anscombe's quartet, this paper introduces Rashomon's quartet, a synthetic dataset for which four models from different classes have practically identical predictive performance. However, their visualization reveals drastically distinct ways of understanding the correlation structure in data. The introduced simple illustrative example aims to further facilitate visualization as a mandatory tool to compare predictive models beyond their performance. We need to develop insightful techniques for the explanatory analysis of model sets.