Using Machine-Learning Systems to Improve Collections Development and Services

Leading members of the Institute of Museum and Library Services-funded Collaborative Collections Lifecycle Project (CCLP) led by the National Information Standards Organization (NISO), Lehigh University, and Partnership for Academic Library Collaboration & Innovation (PALCI), have explored possible future applications of generative artificial intelligence (AI), specifically large language models (LLMs), to optimize library workflows and research related to collection development and management in an innovative network-first infrastructure. Envisioned applications that use LLMs to apply expansive and multi-lingual vocabularies to improve metadata and item processing and identify items for inclusion in approval plans or acquisition processes. Other machine learning applications could be used for analyzing behavioral data or to provide prospective recommendations in content discovery. These capabilities may allow library and other scholarly communication actors to work together, share data and information in a secure and privacy-protective way, define best practices for safe and ethical applications of these technologies, and to further library values of equitable access to information. The panel will share ideas for concrete tools and experiments to stimulate reflection and debate about the potential role of AI and LLMs in the future of library technology, and we will consider both specific applications to improve the quality and use of publishing and library collection lifecycle management.

Boaz Nadav Manes, Lehigh University
Todd Carpenter, National Information Standards Organization
Carolyn Morris, Ingram
Filip Jakobsen, Samhæng
Sebastian Hammer, Index Data

CNI Pre-Recorded Project Briefing Series
May 2024 Edition
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