Guest Post - The Open Access – AI Conundrum: Does Free to Read Mean Free to Train? - The Scholarly Kitchen

"When the Open Access (OA) movement gained momentum in the early 2000s, its proponents envisioned a world where research findings would be freely available to all readers — breaking down paywalls that limited knowledge dissemination and hindered scientific progress. By the 2010s, an increasing number of countries began to mandate open access publications, arguing that publicly funded research should be freely available to the public. The readers envisaged by proponents of OA were obviously human (academics as well as the wider public). While text mining had been considered as one potential application, they could not foresee the development of large language models (LLMs) which would begin to rapaciously ingest large amounts of text. OA literature has become particularly attractive for AI training precisely because it lacks the legal and technical barriers that might protect traditionally published content....

Current licenses, whether traditional copyright or the various CC-BY options, would benefit from explicitly considering what machine reuse means as opposed to human reuse – or indeed machine-mediated human reuse....

the central issue remains that commercial AI companies extract significant economic value from OA content without necessarily returning value to the academic ecosystem that produced it, while at the same time disrupting academic incentive structures and attribution mechanisms....

The very benefits of research that OA is supposed to make available to society at large may be undermined in the medium term. At minimum, OA licenses could mandate that AI tools trained on OA papers need to include citation capabilities in exchange for the free use of high-quality material, to ensure that the creators of academic are appropriately recognized...."