Whilst your model may not be able to explain to you today why it can operate on future data, if it can explain to you why it works on any data you've already given it, it makes it easier to explain behaviour.
You have to trust it on new data.
You can always verify why it made the choices it did on old data.
That seems like something incredibly helpful, to me.
This makes no sense. Your model typically isn't asked to make predictions on "old data" because it was trained on it. And understanding why it made certain prediction isn't any different on "new" vs old data.
You can look into bias-variance tradeoff, train-test splitting and cross validation to get a better picture of this.
Or, like in the article, you can make use of introspective models that can tell you what dimensions were fitted. This can more easily show you where overfitting is occurring.
You can see the weights of the model and follow the decision network, which allows you to explain why something has occurred in a much clearer fashion.
An introspective model gives you a much clearer picture of why certain choices were made, rather than the fuzzy picture you get running statistical analysis against a black-box'd algorithm.