Formalising Explainability and Interpretability: A Conceptual Framework and Taxonomical Dimensions

Research on explainable artificial intelligence (XAI) has expanded rapidly, yet the concepts of interpretability and explainability remain theoretically unstable and inconsistently defined. This paper addresses that weakness directly through a systematic literature review focused specifically on published definitions of the two concepts. We analyse these definitions by decomposing them into semantic units and reorganising the recurring conceptual material into a structured framework centred on four entities: AI system, Human user, Interaction, and Context. On this basis, we derive a set of high-level taxonomical dimensions along which existing accounts differ, including their formalism, measurability, specificity, and dependence on users, domains, and contexts. Our analysis shows that interpretability and explainability are not cleanly separable in the literature, but are better understood as overlapping regions of a broader multidimensional conceptual space. The paper therefore contributes a methodological approach for semantic analysis in fragmented research areas, a novel conceptual framework for interpretability and explainability, and a taxonomical basis for comparing existing definitions. More broadly, it takes a concrete step towards the formalisation of core XAI concepts and towards a more mature theoretical foundation for the field.