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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. 

Charges

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:




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