  A fundamental research goal for Explainable AI (XAI) is to build models that
are capable of reasoning through the generation of natural language
explanations. However, the methodologies to design and evaluate
explanation-based inference models are still poorly informed by theoretical
accounts on the nature of explanation. As an attempt to provide an
epistemologically grounded characterisation for XAI, this paper focuses on the
scientific domain, aiming to bridge the gap between theory and practice on the
notion of a scientific explanation. Specifically, the paper combines a detailed
survey of the modern accounts of scientific explanation in Philosophy of
Science with a systematic analysis of corpora of natural language explanations,
clarifying the nature and function of explanatory arguments from both a
top-down (categorical) and a bottom-up (corpus-based) perspective. Through a
mixture of quantitative and qualitative methodologies, the presented study
allows deriving the following main conclusions: (1) Explanations cannot be
entirely characterised in terms of inductive or deductive arguments as their
main function is to perform unification; (2) An explanation must cite causes
and mechanisms that are responsible for the occurrence of the event to be
explained; (3) While natural language explanations possess an intrinsic
causal-mechanistic nature, they are not limited to causes and mechanisms, also
accounting for pragmatic elements such as definitions, properties and taxonomic
relations; (4) Patterns of unification naturally emerge in corpora of
explanations even if not intentionally modelled; (5) Unification is realised
through a process of abstraction, whose function is to provide the inference
substrate for subsuming the event to be explained under recurring patterns and
high-level regularities.
