To open or not to open? Tensions between scholarship and AI in publication, sharing, and reuse

The utility of generative artificial intelligence (genAI) tools is in part dependent upon their training datasets. The contents of these training datasets are one of many points of contention with genAI, illustrated by allegations of piracy and copyright infringement, accusations of bias, and attempts to control the ingestion of digital content into genAI databases. Individual researchers and educators are scattered along the continuum from complete opposition to full embrace of genAI and the content it produces. These perspectives encompass not only personal decisions and opinions on the use (or non-use) of tools but also the willingness to “feed” these tools with scholarly outputs such as articles, books, and data. This presentation aims to overview some of these major points of tension from the perspective of open scholarship. We will briefly discuss copyright and licensing to foreground the conversation around author rights, reuse permissions, and reciprocity. We will then present perspectives on open scholarship, focusing particularly on motivations for publishing content openly and how these motivations can clash with expectations and behavior from genAI tools and companies. Finally, we will share a few approaches that have been proposed to tackle specific problems in these areas.