Are we undervaluing Open Access by not correctly factoring in the potentially huge impacts of Machine learning? — An academic librarian’s view (I) | by Aaron Tay | Dec, 2022 | Medium

"Synopsis : I have recently adjusted my view to the position that the benefits of Machine learning techniques are more likely to be real and large. This is based on the recent incredible results of LLM (Large Language models) and about a year’s experimenting with some of the newly emerging tools based on such technologies. If I am right about this, are we academic librarians systematically undervaluing Open Access by not taking this into account sufficiently when negotiating with publishers? Given that we control the purse strings, we are one of the most impactful parties (next to publishers and researchers) that will help decide how fast if at all the transition to an Open Access World occurs. For example, should we be willing to even pay a bit more so that we can transition to a Open Access world from a subscription world faster albeit at higher outflows from us?... Free access to all scientific knowledge for all humans is a great boon but this benefit may be dwarfed by the possibilities of using Deep learning and AI on Open Access papers to improve research!..."