Do you think a carpenter who hits themselves with a hammer blames the hammer or themselves? Or are you going to unironically tell me they would blame the hammer + human system?
The discussion is about AI partially replacing human colleagues. In practice, people already are not 100% reliable. You make a reasonable request and someone makes a stupid blunder instead. That's the "hammer" hitting your thumb. Maybe you were not specific enough or maybe they didn't listen but the damage is done.
Our work processes already take mistakes and iterative refinement into account. If AI, in some specific niche, is cheaper and makes no more mistakes than humans do, it gets the job.
It doesn't need to be perfect or perfectly reliable. Some guardrails will be built into it, and we'll come to trust over time.
Please explain how?
>Or are you going to unironically tell me they would blame the hammer + human system?
As your tooling gets more complex, yes it is very easy to have a non-zero blame assignment to each party. Look at any human+machine system where complex failure conditions can occur.
One is a hammer. The other is a human wielding a hammer.
Either way, when working with humans we already deal with plenty of misses and mistakes. Programmers create 10 to 20 bugs per 1000 lines of code, 9 out of 10 businesses fail, accountants make detrimental blunders, etc.
The point is that in the end the ML systems need only to replace these already non-perfect systems. I'll refrain from judging the consequences of this as I think it's out of scope.