Extraction of Semantically Coherent Rules from Interpretable Models

With the emergence of various interpretability methods, the quality of the interpretable models in terms of understandability for humans is becoming dominant. In many cases, interpretability is measured by convenient surrogates, such as the complexity of the learned models. However, it has been argued that interpretability is a multi-faceted concept, with many factors contributing to the degree to which a model can be considered to be interpretable. In this paper, we focus on one particular aspect, namely semantic coherence, i.e., the idea that the semantic closeness or distance of the concepts used in an explanation will also impact its perceived interpretability. In particular, we propose a novel method, Cognitively biased Rule-based Interpretations from Explanation Ensembles (CORIFEE-Coh), which focuses on the semantic coherence of the rule-based explanations with the goal of improving the human understandability of the explanation. CORIFEE-Coh operates on a set of rule-based mode ls and converts them into a single, highly coherent explanation. Our approach is evaluated on multiple datasets, demonstrating improved semantic coherence and reduced complexity while maintaining predictive accuracy in comparison to the given interpretable models.