I think most people agree in the deep learning community that ways to understand NN's are useful, if for nothing other than debugging, but I don't think the community agrees at all over whether explainability is necessary to use it.

In many cases, it depends on the context. For self driving cars, most companies only use neural networks for components of the cars and an explainable algorithm on top of that that interprets data from the components. If there was a crash, they can tie it down to "the neural network parsing the LIDAR did not classify the stop sign correctly, so the driving algorithm did not stop" without needing much interpretability of the neural network directly. If it's an end-to-end system, such as the nvidia car which connects the camera inputs directly to a wheel output, then explainability of that network is much more important.

There was a poll I saw on twitter a while ago (can't seem to find now) that had asked if users would prefer to be treated by an AI doctor with low explainability but 99% accuracy, or high explainability but only 70% accuracy. I think context is key here as well. If the expert doctor working in tandem with the explainable system is better than the non-explainable system, you'd want the explainable system. But if the non-explainable system is better even than experts, I think most people would want the non-explainable system.