Understandability of ML models and it's applications

This Michael Kaminsky post called The Blacker the Box nail the whole point about understandability x formal modelling using the speed of feedback as a mechanism to help to decide the best approach to implement these models. This quote’s about the Fast feedback the author defines as “1) the ability to quickly evaluate the correctness of a prediction1 and 2) the ability to play the game near infinite amounts of time2. “:

If I am designing an application for optimizing landing-page content for my e-commerce site (i.e., choose the content that converts best), then I do not care if my data scientist has rigged up a prediction pipeline that involves passing a SVM prediction through an octopus so long as that model out-performs every other model we have tested in our production environment and we have confidence that we are measuring performance correctly.

The key to this scenario is that I am able to quickly and easily evaluate the performance of candidate prediction models and compare the current production model to new candidate models for evaluation. Because I have an objective measure of predictive success, I do not need any understanding of what the model is doing under-the-hood in order to make use of it.