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I have heard approaches described that train a simpler ML algorithm to predict why a complex ML algorithm made a decision. Decision-tree models predicting neural net models. Paypal or Square I believe.

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how does this solve anything? if a simple decision tree could predict the outputs of more complex deep nets, why not use the decision tree in the first place? also, what do you do when a decision tree isnt powerful enough, as in the case of many interesting problems such as speech, computer vision, etc.

The uses cases for this were around fraud detection systems where you need to provide a reason for the flag.

I don't have to know the complexities of how all the factors are interacting within a neural net to be able to tell someone that if they increased their credit score by 100 points, they'd be approved.

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