Former
The preceding group: Task Group on Strategic Planning for AI and Machine Learning (Dec. 2023-Apr. 2024)
What's New?
- Final report (submitted to PoCo for approval, October 16, 2025)
- PCC AI Resources (This document provides learning resources on AI and metadata and has been updated as of September 30, 2025. Given the rapid evolution of AI, the list is not intended to be comprehensive. To recommend additional resources, please submit feedback through the PCC feedback form. )
- AI/ML (machine learning) definitions and relationships, used in the final report, as of September 30, 2025
- AI Community of Practice (AI-COM) pilot meeting (Recording and Chat, August 5, 2025)
- Interim report (May 2025, approved by PoCo, June 12, 2025)
- Draft PCC Guiding Principles for Use of AI and Machine Learning Technologies in Cataloging and Metadata Work, approved by PoCo on Mar 13, 2025; See the final report for the final version of the guiding principles
Note: This is one of several documents that will be produced by the PCC Task Group on AI and Machine Learning for Cataloging and Metadata and is intended for use with forthcoming documentation on best practices, resources, etc. that will be developed in the coming months. Please provide feedback on the principles outlined using this formbefore August 18, 2025.
Charge
Background
In December 2023, the Task Group on Strategic Planning for AI and Machine Learning was charged with conducting an environmental scan on the impact of AI and Machine Learning on library cataloging and metadata operations and providing evidence on potential directions for PCC. The Task Group submitted the final report to PoCo on April 15, 2024, based on the outcomes of survey results. On May 9, 2024, the PCC Policy Committee (PoCo) approved the Task Group on Strategic Planning for AI and Machine Learning final report and its recommendations, including the establishment of a new task group to carry out the deliverables identified in the report.
Deliverables
The PCC Task Group on AI and Machine Learning for Cataloging and Metadata is charged to:
- Compose and distribute a statement of principles on the use of AI and Machine Learning technologies in cataloging and metadata work. The goal is to emphasize the need for thoughtful] consideration and strategic planning, highlighting that AI is intended to augment, not replace, the valuable work of cataloging professionals (by automating the more rudimentary and repetitive aspects of metadata creation and making the overall process more efficient), and it is not a viable strategy for reduction of personnel. (timeframe: 3 months) [SD2.1]
- Establish a community of practice for catalogers interested in sharing knowledge and experimenting with AI. This community should be open to both PCC and non-PCC library personnel. (timeframe: 6 months) [SD5.3]
- Reach out to international library communities who have been experimenting with and implementing AI-related cataloging projects, for the purposes of compiling a knowledge base for the PCC that may inform the development of best practices. (timeframe: ongoing) [SD1.1]
- Further flesh out the AI resources on the PCC wiki and update as needed, including AI-related cataloging activities happening within other international library communities. (timeframe: ongoing) [SD5.3]
- Collaborate with NARDAC, ALA, the Library of Congress, the Advisory Committee on Equity, Diversity, Inclusion, Belonging, and Accessibility (EDIBA), and other primary stakeholders in the development of cataloging standards and platforms, in order to mutually consider the impact of AI on the future of cataloging work. (timeframe: ongoing) [SD1.1]
- Develop and distribute a best practices document for incorporating AI and Machine Learning into cataloging and metadata work. (timeframe: 1 year) [SD2.1]
- Work with the Standing Committee on Training (SCT) to develop introductory training resources for learning more about or experimenting with AI. (timeframe: 1 year) [SD5.3]
- Provide a report summarizing the work described above.
PCC Strategic Directions progress 2023- (working document):
- SD 1.1.7. Establish a task group to initiate communication and collaboration between stakeholders involved with the development and implementation of cataloging and metadata applications of Artificial Intelligence (AI) and Machine Learning (ML)
- SD 2.1.6. Support and promote the development of best practices for use of Artificial Intelligence (AI) and Machine Learning (ML) for cataloging and metadata work
- SD 5.3.3. Develop training resources for learning more about or experimenting with AI
Time Frame:
- Date charged: September 3, 2024
- Date preliminary report due: March 3, 2025
- Date final report due: September 3, 2025
Reports to:
PCC Policy Committee
Roster:
- SCA/NLM representative: Alvin Stockdale (NLM, alvin.stockdale@nih.gov), co-chair (Sept 3, 2024-Jan 22, 2025)
- SCA representative: Mollie Coffey (Spokane Public Library, mcoffey@spokanelibrary.org) (Mar 10, 2025- )
- SCS representative: Liz Miraglia (California Digital Library, elizabeth.miraglia@ucop.edu), co-chair (Mar 10, 2025- )
- SCT representative: Kate James (OCLC, jamesk@oclc.org) & OCLC representative (May 24, 2025- )
- Task Group on Strategic Planning for AI and Machine Learning, former member: Caroline Saccucci (Library of Congress, csus@loc.gov)
- PoCo representative: Christine DeZelar-Tiedman (University of Minnesota, dezel002@umn.edu)
- EDIBA representative: Keno Catabay (University of Colorado Boulder, Keno.Catabay@colorado.edu)
- OCLC representative: Inkyung Choi (choii@oclc.org) (Sept 3, 2024-May 23, 2025)
- Clarivate representative: Lili Daie (Lili.Daie@Clarivate.com)
- Library of Congress representative: Heidy Berthoud (hberthoud@loc.gov)
- Joint RDA Board and RSC Working Group on AI representative: Charlene Chou (New York University, charlene.chou@nyu.edu), co-chair
- Volunteer in the survey: Serena Cericola (Casalini Libri Spa, serena.cericola@casalini.it)
- DCMI Education Committee representative: Inkyung Choi,cik860@gmail.com) (May 24, 2025- )
- Consultant: Jenny Toves (OCLC, tovesj@oclc.org) (Sept 3, 2024-Jan 31, 2025)