ML tending to make weird mistakes rather than subtle ones that make sense in context like human transcribers is likely to make them easier to spot.

And there are humans in the loop too, and an enormous amount of redundancy in the questions and answer, so even plausible false transcriptions will get picked up on if they matter. Nobody gets sent to jail simply because the transcription process - human or machine - accidentally substitutes "I did it" in place of "I didn't" midway through a two hour interview.

+1. There is a widespread "metric fallacy" or "task fallacy" going around. Models of course optimize for metrics, so they tend to perform well on those related metrics.

Humans, however, are not simply metric optimizers. Though it's always in the interest of those corporations producing metric optimizers (i.e. models) to paint humans as such, so their models shine in comparison. They want humans to look like bad machines, so it looks like they should be automated. Not to say they shouldn't in many cases, just that there's a clear one-sidedness in all corporate PR (and funded research, especially that research which is also PR).

All this to say that yes I agree with you. And if we humans don't want our unsustainable economic growth to turn us even more into machines (as our bureaucratic creep has done quite well thus far), we should fight such rhetoric that aims to paint humans simply as machines or task-doers.