"Interpretability", in this case, is concerned with the problem domain, not ML itself. A hypothetical example: A trained decision tree algorithm for a medical

"Interpretability", in this case, is concerned with the problem domain, not ML itself. A hypothetical example: A trained decision tree algorithm for a medical decision may place the gender, or the age, of the patient at the root. That lends itself to a quick interpretation as to the relevance of factors for treatment, whereas with a Neural Net, you'll get millions of arbitrary floats that do not impart any meaning just by looking at them. That's not to say NNs don't sometimes give tantalising insights, as the article points out. I've seen a view visualisations of generative character models that were fascinating–such as finding individual neutrons tracking sentence length or nesting depth. Same for some interesting patterns emerging in the intermediate layers of object recognition networks: oh, I never knew ears were so important for face recognition.