Seeing this reminded me of an episode of TWiML&AI [0] (This week in machine learning & AI podcast). It features a very interesting discussion of how Stripe prov

Seeing this reminded me of an episode of TWiML&AI [0] (This week in machine learning & AI podcast). It features a very interesting discussion of how Stripe provides an explanation for their black box fraud model to customers when it chooses to block transactions. They basically have created an explanatory model which they train against the black box model and then run against the fraudulent transaction. It outputs the first explanation from a list of human interpretable explanations that would have changed the fraud decision from "Fraud" to "Not Fraud." This is one of the most interesting episodes in the show based on the number of times it has come up in conversation. In looking up the show notes for that episode, I see that there is also a mention of this paper (in the OP), and that the authors were previously interviewed in TWiML #7 (which I haven't listened to). 0: https://twimlai.com/twiml-talk-73-exploring-black-box-predictions-sam-ritchie/ https://twimlai.com/twiml-talk-73-exploring-black-box-predic...