Humans are hardly ever 99% accurate instantaneously on classification problems either. Humans have the ability to know when they should collect more information though, and can often perform some sort of hedging action when they are unsure.
But isn't also the case that humans understand the consequences and the depth of multi-variable decisions?
For example, Amazon Seller Central, Google, Youtube, can "outsource" their customer service to AI, because they are pretty much the only players, so customers have to suck it and deal with the frustration of a terrible experience by not getting help and not talking with a human.
With any other business if they get automated replies that don't solve a customer issue and they are unable to talk with a human, 99% of them say "fuck it" and go somewhere else.
This is one small realm of the relationship of AI and businesses. Then you have employees, suppliers, supply chains, finances, internal processes, so many multi-variable, fragile and nuanced systems that I doubt if they're not developed in-house, they'll probably do more damages than solve problems.
This is interesting to reason about because it's may even be true that the human and AI error rates could be the same. In fact the human error rate might even we worse, but the kinds of errors and their impact makes a big difference.
I can only speculate, but while it's true humans can fail when following a process, it's also true they can sometimes spot a potential failure even then there is no process to prevent it. They can come up with new processes, or ways to improve existing ones. They can also account for their actions, and managers can account for the activities of their team. All of this builds trust that the process can be improved in ways that can be well understood.
People are also scalable. You can implement controls like four-eyes on changes and critical metrics, so you're less exposed to one person's idiosyncrasies. It's hard to do that with AI.
I agree. Humans are interactive agents, they have broader knowledge about the world, and they can (to a degree) diagnose their performance and compensate.
Think of a DL powered, visually guided robot vs. a human on a production line.
One task might be to do QC inspection at the end of the line. Suppose something gets on the camera lens. In general, the DL system will keep chugging along and the accuracy will degrade. The human will notice this and clean his glasses.
If he sees something ambiguous as it passes on the line, he might give it a bit more attention or adjust the angle he's looking at it from. If he sees a series of the same anomalies, he will notice a pattern. Perhaps one of the machines up the line from him has started to introduce a new type of defect.
Suppose in assembly a worker has a sore muscle. He might adapt his motions to compensate, slow down, go to the doctor, take some pain pills, or take a day off. Unless programmed or trained to detect this, a robot will keep driving its failing motor harder until it breaks.
