What is the difference between explainable and interpretable machine learning?
I found this article by Cynthia Rudin which goes a bit more into the difference between the two terms that is in line with your source from O'Rourke.
At the core it is about the time and mechanism of the explanation:
A priori (Interpretable) vs. A posterio (Explainable)
I found this quote to be very helpful and inline with my own thoughts (emphasis mine):

Rather than trying to create models that are inherently interpretable, there has been a recent explosion of work on ‘explainable ML’, where a second (post hoc) model is created to explain the first black box model. This is problematic. Explanations are often not reliable, and can be misleading, as we discuss below. If we instead use models that are inherently interpretable, they provide their own explanations, which are faithful to what the model actually computes.

In short an interpretable model is able to output humanly understandable summaries of its calculation that allow us understand how it came to specific conclusions. Due to that a human would be able to actually create a specific desired outcome by selecting specific inputs.
A &quot;merely&quot; explainable model however does not deliver this input and we need a second model or mode of inspection to create a &quot;Hypothesis about its mechanism&quot; that will help explain the results but not allows to rebuild results by hand deterministically.