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Calls to arms to build interpretable models express a well-founded discomfortwith machine learning. Should a software agent that does not even know what aloan is decide who qualifies for one? Indeed, we ought to be cautious aboutinjecting machine learning (or anything else, for that matter) intoapplications where there may be a significant risk of causing social harm.However, claims that stakeholders "just won't accept that!" do not provide asufficient foundation for a proposed field of study. For the field ofinterpretable machine learning to advance, we must ask the following questions:What precisely won't various stakeholders accept? What do they want? Are thesedesiderata reasonable? Are they feasible? In order to answer these questions,we'll have to give real-world problems and their respective stakeholdersgreater consideration.

The Doctor Just Won't Accept That!