https://www.linkedin.com/pulse/untenable-middle-ground-responsible-ai-use-emily-m-bender-8jyfc/
So what is the best way out of that uncomfortable, untenable space? I think one key step is disaggregating the (non-coherent) set of technologies sold as "AI". If you don't call the stuff you work with "AI", you aren't saddled with trying to defend any of the rest of it.
The most recent iteration of this conversation I was involved in turned in part on a strange, over-expansive definition of "genAI" which included, for ex, optical character recognition (OCR).
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.
I interviewed at two different glorified-OCR startups pre-pandemic (?pre-AI?) for an ML role, and neither CTO knew what a spline was. That is my OCR story.