Semantic Reasoning from Model-Agnostic Explanations

Abstract

With the wide adoption of black-box models, instance-based post hoc explanation tools, such as LIME and SHAP became increasingly popular. These tools produce explanations, pinpointing contributions of key features associated with a given prediction. However, the obtained explanations remain at the raw feature level and are not necessarily understandable by a human expert without extensive domain knowledge. We propose ReEx (Reasoning with Explanations), a method applicable to explanations generated by arbitrary instance-level explainers, such as SHAP. By using background knowledge in the form of ontologies, ReEx generalizes instance explanations in a least general generalization-like manner. The resulting symbolic descriptions are specific for individual classes and offer generalizations based on the explainer's output. The derived semantic explanations are potentially more informative, as they describe the key attributes in the context of more general background knowledge, e.g., at the biological process level. We showcase ReEx's performance on nine biological data sets, showing that compact, semantic explanations can be obtained and are more informative than generic ontology mappings that link terms directly to feature names. ReEx is offered as a simple-to-use Python library and is compatible with tools such as SHAP and similar. To our knowledge, this is one of the first methods that directly couples semantic reasoning with contemporary model explanation methods. This paper is a preprint. Full version's

Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

“…Reasoning with Explanations (ReEx) [46] to discover their hypernyms, which can serve as human understandable explanations. Figure 7 shows words with the highest variance in their respective class, while Figure 8 shows found hypernyms of words with the highest variance for each of the classes.…”