  Despite explainable AI (XAI) has recently become a hot topic and several
different approaches have been developed, there is still a widespread belief
that it lacks a convincing unifying foundation. On the other hand, over the
past centuries, the very concept of explanation has been the subject of
extensive philosophical analysis in an attempt to address the fundamental
question of "why" in the context of scientific law. However, this discussion
has rarely been connected with XAI. This paper tries to fill in this gap and
aims to explore the concept of explanation in AI through an epistemological
lens. By comparing the historical development of both the philosophy of science
and AI, an intriguing picture emerges. Specifically, we show that a gradual
progression has independently occurred in both domains from logical-deductive
to statistical models of explanation, thereby experiencing in both cases a
paradigm shift from deterministic to nondeterministic and probabilistic
causality. Interestingly, we also notice that similar concepts have
independently emerged in both realms such as, for example, the relation between
explanation and understanding and the importance of pragmatic factors. Our
study aims to be the first step towards understanding the philosophical
underpinnings of the notion of explanation in AI, and we hope that our findings
will shed some fresh light on the elusive nature of XAI.
