Agreed. Also, nobody is, or should be, using deep neural networks, for legislation and law enforcement. Explainability should be a core design decision when making an algorithm, and not slapped on top of an inherently black box algorithm. Black boxes and even their explanations are used to launder bias and unfairness. And most of these tricks are not even explanations that can be trusted. "Oh look, the cat's head is highlighted, so that's why this picture was classified as a cat!" no insight, no justification, just hoping the network learned some higher level features like humans do, but oh no, when we flip the picture it is suddenly a dog, and when we photoshop the background to be snow, now it is suddenly a polar cat or a pinguin.

Let deep learning do what it is good at, without explaining their performance and errors to anyone: invading your privacy on social networks, helping hedge funds make more money by analyzing Elon Musks tweets, and building military surveillance.

Leave the justifications and explanations to inherently white box models (they are nearly as good in performance as black box now, at least for structured data), and hold off on firing radiologists for a few decades, even though your train set performance is overfitted to be on par with "human-level".

Somehow, somewhere, the deep learning revolution started to drink its own kool-aid and became alergic to critique or solid verifiable computer science. Explainable deep learning does not exist, since half of the time the engineer that build the system can't even explain why it works in the first place. "Strong inspectable feature engineering is hard and time-consuming, so here we shook a box of legos a million times, burned six holes in the ozon layer, and out comes a deep net optimized with gradient descent". End-to-end learning is supposed to be really end-to-end, including the explanation.