By the way many methods are excellent at explaining their decisions. The favorite is decision trees which break the decision up into discrete decisions for individual features. E.g. a person was put in class 1 because their income was above X, their savings was above Y, etc. Other "shallow" methods like regression are fairly easy to interpret as well.

It is mainly deep learning for which this is difficult. Which I'm sure is the method in the twitter discussion, but that is a different kind of problem where interpretability doesn't have a clear use.