XAI research talks about exactly that, system B "surrogate models" that are consistent with the recommender right around the locus of a prediction. If the surro

XAI research talks about exactly that, system B "surrogate models" that are consistent with the recommender right around the locus of a prediction. If the surrogate model is easy to interpret, you can forgo directly explaining the base recommender. I think a thing to keep in mind that recommenders may be proprietary, and thus pointedly black box. I view interpretability as a conversation between the recommender operator and the user, subject, or regulator of the recommender that is unfortunately mediated by confidentiality. I view explanation as like debugging, something you do in house in dev, and it bewilders me that one would launch a system that one can't explain. But, sometimes we make deployments first and then rationalize them after ...