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In December 2023, the Task Group on Strategic Planning for AI and Machine
Learning 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 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
resultssurvey 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
recommendationsits recommendations, including the establishment of a new task group to carry out
the deliverables identified in the report.
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The PCC Task Group on AI and Machine Learning for Cataloging and Metadata
is Metadata is charged to:
- Compose and distribute a statement of principles on the use of AI and
Machine and Machine Learning technologies in cataloging and metadata work. The goal
is to emphasize the need for thoughtful consideration and strategic
planningstrategic planning, highlighting that AI is intended to augment, not replace, the
valuable work of cataloging professionals (by automating the more
rudimentary 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 reduction of personnel. (timeframe: 3 months) [SD2.1] - Establish a community of practice for catalogers interested in sharing
knowledge 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 been experimenting with and implementing AI-related cataloging projects, for
the purposes of compiling a knowledge base for the PCC that may inform
the 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 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 Advisory Committee on Equity, Diversity, Inclusion, Belonging, and Accessibility
(EDIBA), and other primary stakeholders in the development of cataloging
standards 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 and Machine Learning into cataloging and metadata work. (timeframe: 1 year)
[SD2.1] - Work with the Standing Committee on Training (SCT) to develop
introductory 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.
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- SD 1.1.7. Establish a task group to initiate communication and
collaboration and collaboration between stakeholders involved with the development and
implementation of cataloging and metadata applications of Artificial
Intelligence Artificial Intelligence (AI) and Machine Learning (ML) - SD 2.1.6. Support and promote the development of best practices for use
of 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 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
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