When someone is trying to hold this untenable position, a few things tend to come up (not everytime, not everyone):

1- Defensiveness. People read criticism of the systems and proposed uses of the systems as accusations that users are "bad people". Thus a criticism of the tech lands as criticism of the user, and tensions flare.
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4- Tone policing. People who are trying to occupy that uncomfortable, untenable space will claim that clear statements of harms/strong principles against use of these systems will "turn others away" as if the centrists are the ones actually pushing for more ethical practice.

But this "other people won't listen" remark I think is really a way of saying "This makes me uncomfortable" while trying to claim to be on the right side of history at the same time.
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OCR can be a useful tool for many research projects! OCR is also the kind of technology that gets better with better language models, i.e. more fine-grained models of which word(parts) go where. That has been true since before "genAI" and will be true after.

Just because you can use the synthetic media extruding machines to approximate the task of OCR, however, doesn't mean that that task can or should be used to justify the use of "genAI" in research.
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I think another important step is a values examination. What is important to you? How are those values supported or not by entering the discourse in a way that holds space for OpenAI/Anthropic/Google/Meta and all the other actors in this massive push to shove "AI" into every part of our lives as "not all bad"?

What are your research goals, what do you value about participating in scholarship, how can you meet those goals/act in accordance with those values and what obstacles are in your way?
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Part of what makes that middle ground untenable and uncomfortable, I think, is that it requires carrying water for these clearly bad actors. You can set that bucket down and step out onto firmer ground.

This does require going against the mainstream, but that gets easier when a) you find you're not alone and b) you see how much of mainstream opinion on this is actually the result of marketing.

/fin for now

@xvf17 @emilymbender THIS IS SO UPSETTING. I am one of those folks advocating for using certain types of machine learning responsibly, like "identify the patients with cancer" which ML tools can do better than humans, and which were trained using ethical data sources.

But the LLM field is just a cesspool. I can *see* use cases for it, but holy shit fruit of the corrupt root system with that training data.

@SomeVeganCheeseIsOk
Oh no dude. This prof thinks you are an asshole then.

Read what she wrote again. She is full of fallacies herself.
@xvf17 @emilymbender