It doesn't really have to be perfect as long as it doesn't fail in common scenarios.
In practice it probably doesn't matter anyway - the chance of the exact required perturbation of the input happening by chance are infinitesimal, due to the high dimensionality of the input. And even if it was a problem there are ways around it.
This is a good question. My impression is that humans fail and artificial neural networks fail but we don't know enough about the brain to say artificial neural networks fail in the same way as humans.
As another poster notes, humans accept human error more than computer error and I think that's because humans have an internal model of what other humans will do. If I see a car waving in a lane and going slowly, I have some ideas what's happening. I don't think that model would extend to a situation where neural network-driven car was acting "wonky".
Have you seen the AI Formula 1 called roborace? Once those cars get good enough to beat Lewis Hamilton or Seb Vettel I'll trust it with me and my family.
i agree that it doesn't have to be perfect, but the standard should be higher than "doesn't fail in common scenarios." we should also expect graceful handling of many uncommon but plausible scenarios. we expect human drivers to handle more than just common scenarios, and human drivers are pretty bad.