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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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