Not all explanations are causal. The explanation literature in the philosophy of science goes pretty far back, but here are some of the highlights:

The Deductive Nomological Model (Hempel and Oppenheim, 1948) tries to explain a phenomenon using a deductive argument where the premises include particular facts and a general lawlike statement (like a law of nature) and the conclusion is the thing to be explained.[1]

The Statistical Relevance Model (Wesley Salmon) attempts to fix some shortcomings in the DN model that allowed explanations using particular facts and general laws that were not at all relevant to the phenomenon being explained. The idea is that you can explain why X hasn't become pregnant by saying that X has taken birth control, and people who take birth control do not become pregnant, and that would fit the DN model, but this explanation is not statistically relevant if X is male.[2]

Unificationist accounts (Philip Kitcher) seek to unify scientific explanations under a common umbrella as was done with, e.g. electromagnetism. If it is possible to have a unified theory of something, each element becomes more explainable based on its position within that unified theory [3]

pragmatic and psychological accounts tend to fit more closely with the kinds of rationalizations that we've seen as some explanations of AI. They can be fictional, but they don't have to be [4]

IMO we don't currently have an adequate account of explanation within the philosophy of science that works for deep neural networks. This is what my dissertation research focuses on.

[1] https://en.wikipedia.org/wiki/Deductive-nomological_model

[2] https://plato.stanford.edu/entries/scientific-explanation/#S...

[3] https://plato.stanford.edu/entries/scientific-explanation/#U...

[4] https://plato.stanford.edu/entries/scientific-explanation/#P...

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