CASTLE: Cluster-aided space transformation for local explanations. (1st October 2021) - La Gatta, Valerio; Moscato, Vincenzo; Postiglione, Marco; Sperlì, Giancarlo
Highlights: CASTLE aims to combine local and global features to explain any AI system predictions. Model's global knowledge, as rules, supports the local explanation generation process. CASTLE estimates how clusters' representative points in uence predictions. It supports any explanation type (e.g. rule-based, feature importance, counterfactual). Abstract: With Artificial Intelligence becoming part of a rapidly increasing number of industrial applications, more and more requirements about their transparency and trustworthiness are being demanded to AI systems, especially in military, medical and financial domains, where decisions have a huge impact on lives. In this paper, we propose a novel model-agnostic Explainable AI (XAI) technique, named Cluster-aided Space Transformation for Local Explanation (CASTLE), able to provide rule-based explanations based on both the local and global model's workings, i.e. its detailed "knowledge" in the neighborhood of the target instance and its general knowledge on the training dataset, respectively. The framework has been evaluated on six datasets in terms of temporal efficiency, cluster quality and model significance. Eventually, we asked 36 users to evaluate the explainability of the framework, getting as result an increase of interpretability of 6 % with respect to another state-of-the-art technique, named Anchors.