True, they're similar... But what's also similar is that people make the mistake of focusing on differences in failure rates while glossing over failure modes.

Human imperfections are a family of failure-modes which have a gajillion years of experience in detecting, analyzing, preventing, and repairing. Quirks in ML models... not so much.

A quick thought-experiment to illustrate the difference: Imagine there's a self-driving car that is exactly half as likely to cause death or injury than a human driver. That's a good failure rate. The twist is that its major failure mode is totally alien, where units attempt to inexplicably chase-murder random pedestrians. It would be difficult to get people to accept that tradeoff.

No, people have the correct intuition that human errors at human speeds are very different in nature from human rate errors at machine speeds.

It's one thing if a human makes a wrong financial decision or a wrong driving decision, it's another thing if a model distributed to ten million computers in the world makes that decision five million times in one second before you can notice it's happening.

It's why if your coworker makes a weird noise you ask what's wrong, if the industrial furnace you stand next to makes a weird noise you take a few steps back.