Smart enough to mislead
the functional shortcomings and ethical dilemmas of generative AI use in metadata work
Keywords:
generative AI, metadata creation, metadata enhancement, AI ethicsAbstract
This article critically examines the applicability of generative AI in library metadata creation and cataloguing, arguing that despite growing interest and experimentation, such technologies remain fundamentally unsuited for this domain. Drawing on recent literature, surveys, and institutional case studies, the author demonstrates that generative AI tools consistently produce metadata outputs that are unreliable, inconsistent, and ethically problematic. While machine learning offers potential in specific, supervised metadata functions, generative AI’s reliance on probabilistic outputs, lack of transparency, and tendency to hallucinate undermine the accuracy and reliability essential to cataloguing. The article also explores the broader ethical implications of AI adoption in libraries, including issues of bias, environmental impact, copyright concerns, and labour exploitation. The author argues that fully automated metadata creation using generative AI is neither technically viable nor ethically responsible and instead advocates for cautious, critically informed AI integration, emphasising the continued necessity of human oversight and ethical scrutiny in metadata work.
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