It's easy to bring down the hammer of scope insensitivity by vividly describing a single person's struggle. The question is how AI affects error rates.
If I report a bug in (for example) Slack that loses messages and cost me a lot of time and headaches, is it an appropriate response to say "but Teams has even more bugs" or "sure, but if you had that conversation face to face you might miss something too"?
And hence comparing AI use to a fictional situation where no errors happens is not meaningful.
I've personally caught multiple errors that were not just transcription errors, but elementary reasoning errors done by specialists I've seen - the baseline error rate from healthcare providers is far above zero.
I treat it as it is: a trendy hit piece against AI. There's nothing like "Why they still use Whisper Large V2?" in sight. If this article pushes the establishment to be more transparent about the AI tools, good. But right now there's not much to discuss.
My experience dealing with ai-mediated processes is that the error recovery paths often simply don't exist, presumably because eliminating the personel that dealt with oddball and errors was the supposed benefit of having the AI deal with it in the first place.