They are trying to create a distinction, not between the explainability of machine learning approaches, but between human approaches and machine learning approa

They are trying to create a distinction, not between the explainability of machine learning approaches, but between human approaches and machine learning approaches. Whatever paper you think I should be using for my definition, just go to there citations. You'll trace it back eventually to things like the Imitation Game paper by Alan Turing. You'll trace it back to the debate about whether machines can think, discover, learn, know. The papers you reference are built atop a mountain of papers that came before them which largely reject the claim that machines aren't capable of discovery. You'll also find my term usage early on in most books about machine learning. Most authors like introducing machine learning by pointing out why we need machine learning. They point out that humans not being able to explain solutions to very hard problems means they can't build programs that solve those problems. If they could explain their solution, they could code it up and they would have a program. Since they can't, they reach to learning tools to figure it out. It really shouldn't surprise you that we have to go back to the early parts of the field; his argument restated is "machines can't make discoveries, only humans can" and this confused thinking is something that gets addressed in the early writings much moreso than the latter writings. Frankly, we've known algorithms as far back as Aristotle that were capable of discovery. Peter Norvig in AIMA cites Aristotle when talking about some search problems and path discovery is still discovery. Some of these algorithms literally include frontier in the algorithmic description; clearly a framing that implies thinking of them in terms of discovery. Honestly, what is really happening in my estimation is some people do this thing where they pretend AI is categorically reduced to the subset of things which they can criticize. It is a really weird tendency, but extremely common. You can see it in the persons post actually. Look at the contortions they go through to only focus on supervised learning - to only focus on a narrow subset of supervised learning restricted to deep learning - to only focus on a narrow problem domain of supervised learning - classification - and to only focus on a specific example of that problem domain (we can give good explanations for smaller models) - the ones that humans can't easily explain.