Library Collections and Academic Publications as Artificial Intelligence Training Data

What principles should guide libraries as they consider allowing or limiting access to their collections for artificial intelligence (AI) training? US research libraries collectively hold one of the world’s most comprehensive, sophisticated, and structured reflections of human thinking. In addition, research libraries steward the research output of the university, and support and advise their authors on their publishing choices. This published literature and these library collections are incredibly valuable for the development and deployment of AI. The opportunity to advance research and learning through AI is great but comes with significant risks. This panel will discuss balancing legal, technical, logistical, and mission-oriented challenges and opportunities and will explore what principles should guide decision-making for AI use of the universities’ scholarly content.

Dan Cohen, Northeastern University
Mike Furlough, HathiTrust
David Hansen, Authors Alliance
Claire Stewart, University of Illinois Urbana-Champaign
Günter Waibel, University of California Office of the President
Suzanne Wones, University of California, Berkeley

Learn more: https://www.cni.org/mm/fall-2024/project-briefings-f24

Coalition for Networked Information (CNI)
Presented at Fall 2024 Membership Meeting
December 9-10, 2024

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