What if an interpretable model is worse at telling stop signs from jackets than an uninterpretable model? Should we use the worse model because we value interpretability?
This is the type of hypothetical that kills the discussion, though.
If the model is interpretable, you have a high chance of knowing why it does or does not tell a stop sign from a jacket. If it is not, you only know that in your test/validation set, it can do the job.
Even tasks that machine learning clearly excels at is currently in a state where all good uses of it has a human supervisor at some level. Recognizing faces, as an example. For my personal library, I absolutely have to disambiguate the recognized faces of my kids as they get older in all of the products I've used.
If we value interpretability for the particular model, e.g. as in the loan example or where by law you have to make sure race was not a consideration, I'd say yes. In places where interpretability has no additional value, than of course no.
But it of course depends on the exact value trade-off, which any model designer already has to consider.
Yes, because in the interpretable model, the fix can also let you check robustness against flags and mailboxes at the same time. Not everything is correctly captured in the test dataset, so we need levels of abstraction that let us be more general.
You can use an uninterpretable model in conjunction with a post-hoc explainer—and in fact, this is most often how explainers are used. This gives you the best of both worlds: powerful models and auditability for their decisions.
