> What matters is the percentage accuracy. A black box with a 10% failure rate is better than a fully explainable system that fails 20% of the time.

That's not true at all, it depends on the use case.

What actually matters is the desired percentage of acceptance.

For many critical path use cases you'd much rather have something fail twice as often but understand why it failed so that you can correct the issue and resubmit the input. Error observability is an important feature that's taken for granted in many systems. It all depends on what the system is used for—how important it is to be able to get to correct results, and what the consequences for failure are. The biggest danger of neural networks is in people that don't understand this nuance and apply them in a blanket way in all systems.