I have a small collection myself that I can post after I get some sleep, but IT is pushing for more AI usage and I would like to supply them with studies showing the negative cognitive, psychological, and social impacts
I have a small collection myself that I can post after I get some sleep, but IT is pushing for more AI usage and I would like to supply them with studies showing the negative cognitive, psychological, and social impacts
Here’s an academic article titled xm"AI and the problem of knowledge collapse". It’s paywalled though, so DM me if you’d like the pdf.
It looks more at the problem of our collective knowledge being at risk, which I think is a big thing. So much of our institutional knowledge is contained within people, and outsourcing that to AI is just a recipe for disaster on many fronts — not least of all because if an organisation ends up becoming dependent on AI, then it’s just making itself more brittle; if a model is updated, leading to significantly different performance, or it the cost model changes, then that has some big problems.
This next link isn’t an academic study, but hopefully helpful. It’s looking at how many companies are backtracking after the charging model for many AI companies meant costs skyrocketed. If IT gets people to start using AI en masse, are they really willing to be on the hook if the same thing happens with your organisation? AI is still not profitable for the people selling it, so this is unlikely to be the last time that the up the fees
Free preprint: https://arxiv.org/abs/2404.03502
I really like this, thanks for sharing. I think the paper explains the concept much better than the abstract. It’s the inverse of model collapse, maybe I would call it public alignment more than knowledge collapse. The influence of the general public all using a single knowledge model means that diverse opinions and knowledge will be drowned out and median opinions will crystalize, regardless of their accuracy. This is in contrast to scientific progress, which relies on discriminating good and bad arguments, rather than quantifying them.
Thanks for adding the preprint. Also thanks for this summary, you explained it far better than I could (I left my comment in a bit of a hurry)
I think both synopses were valuable! You interpreted it in terms of how a single institution or company could be affected, which is probably more relevant for op.