We have many rules, regulations, strategies, patterns, and legions of managers and management philosophy for dealing with humans.
With humans, they're incorrect sometimes, yes, and we actively work around their failures.
We expect humans to develop over time. We expect them to join a profession and give bad answers a lot. As time goes on, we expect them to produce better answers, and if they don't we have remediations to limit the negative impact they have on our business processes. We fire them. We recommend they transfer to a different discipline. We recommend they go to college.
Comparing the successes and failures of LLMs to humans is silly. We would have fired them all by now.
The big difference is that computers CAN get every single question correctly. They ARE better than humans. LLMs are a huge step back from the benefits we got from computers.
I emphatically disagree on that point. AFAIK, nobody has been able to demonstrate, even in principle, that omniscience is possible over a domain of sufficient complexity and subtlety. My gut tells me this is related to Gödel's Incompleteness Theorem.
b) Experts may give wrong answers but it will happen once. LLMs will do it over and over again.
Well... Sometimes "experts" will give the wrong answer repeatedly.
What gets difficult is evaluating the response, but let's not pretend that's any easier to do when interacting with a human. Experts give wrong answers all the time. It's generally other experts who point out wrong answers provided by one of their peers.
My solution? Query multiple LLMs. I'd like to have three so I can establish a quorum on an answer, but I only have two. If they agree then I'm reasonably confident the answer is correct. If they don't agree - well, that's where some digging is required.
To your point, nobody is expecting these systems to be infallible because I think we intuitively understand that nothing knows everything. Wouldn't be surprised if someone wrote a paper on this very topic.