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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 and providing evidence on potential directions for PCC. The Task Group submitted
the 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 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 out the deliverables identified in the report.
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- 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 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 sharing knowledge and experimenting with AI. This community should be open to
both 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 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 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 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 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 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 and metadata work - SD 5.3.3. Develop training resources for learning more about or experimenting with AI
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