AI for Library Cataloging and Metadata

Library metadata makes materials discoverable and supports sharing records across systems. Cataloging includes structured descriptions, authority control, subject access, classification, and local decisions about how to represent works and communities. AI can assist with extracting titles, generating summaries, suggesting subject headings, detecting duplicates, or identifying incomplete records. It can also introduce errors: an author may be confused with a subject, a translation may distort a title, a generated summary may invent content, or a suggested heading may reflect outdated or biased terminology. MARC fields have defined roles and structure, so a plausible-looking record can still be invalid or misleading. Catalogers should review suggestions against the item, applicable standards, authority files, and local policies. Metadata decisions can affect which works users find and how communities are represented. The American Library Association’s guidance recommends professional judgment and review for AI-generated metadata and discovery features. Libraries should document which fields were machine-suggested, preserve provenance, and offer correction processes. Automated cleanup should not silently merge distinct entities or overwrite human-reviewed records. Evaluation should assess both technical completeness and representational quality across languages, formats, and communities. AI can reduce routine labor and help surface inconsistencies, but cataloging remains an interpretive practice requiring professional expertise and accountability. Communities represented in the catalog should be consulted when descriptions affect identity or sensitive topics. A record should remain correctable when standards or community preferences change.