Devil's advocate: suppose humans succeed only 99% of the time, but we can understand and characterize the 1% of failures fairly well. Now suppose machine learning-based approaches succeed 99.9% of the time and only fail 0.1%, but that 0.1% is for totally unknowable reasons. Is the former better than the latter?

It's fun to imagine someone constructing adversarial examples to cause self-driving cars to crash. But is that really a big deal? Humans can just as easily be tripped up by an "adversarial" example: I can pretty easily point a laser pointer from my window at random humans driving a car. The legal system has well-defined mechanisms for dealing with this.