Taking and Giving Back? Open Access, Generative AI, and the Transformation of Scholarly Communication

Abstract

Generative AI systems trained on decades of open access, digitized scholarly publications and other human-written texts can now produce non-copyrightable(?), (mostly) high-quality, and (sometimes) trustworthy text, images, and media at scale. In the context of scholarly communication, these AI systems can be trained to perform useful tasks such as quickly summarizing research findings, generating visual diagrams of scientific content, and simplifying technical jargon.

Scholarly communication will undergo a major transformation with the emergence of these model capabilities. On the plus side, AI has the potential to help tailor language, format, tone, and examples to make research more accessible, understandable, engaging, and useful for different audiences. However, its use also raises questions about credit and attribution, informational provenance, the responsibilities of authorship, control over science communication, and more. This talk will discuss how open access scholarly publishing has helped power the rise of the current generation of AI systems (especially large language models), some ways that AI is primed to change/has already changed scholarly publishing, and how the OA community might work with these models to improve scholarly communication, for example, by introducing different and more flexible forms of science communication artifacts, incorporating human feedback in the generative process, or mitigating the production of false/misleading information.