AI in Technical Services
For this issue, the editors have decided to try out something a little different for our bibliography section: an annotated bibliography. A brief description of each article follows the citation to give readers a quick glimpse into the contents. We encourage you to reach out to the editors at tcbgroup@myatla.org with thoughts on the new layout.
Anderson, Clifford Blake and Jim Duran. 2024. “Responsible AI at the Vanderbilt Television News Archive: A Case Study.” Journal of eScience Librarianship 13 (1): e805. https://doi.org/10.7191/jeslib.805.
“We provide an overview of the use of machine-learning and artificial intelligence at the Vanderbilt Television News Archive (VTNA). After surveying our major initiatives to date, which include the full transcription of the collection using a custom language model deployed on Amazon Web Services (AWS), we address some ethical considerations we encountered, including the possibility of staff downsizing and misidentification of individuals in news recordings.” [Abstract]
Berkowitz, Adam. 2024. “‘Gimme Some Truth’ AI Music and Implications for Copyright and Cataloging.” Information Technology and Libraries 43 (1): 1-15. https://doi.org/10.5860/ital.v43i3.17072.
A “critical textual analysis and qualitative content analysis” of the development of policies regarding the authorship of AI-generated or AI-assisted works by the US Copyright Office and the PCC. These policies are explored through case studies which consider the implications for AI music.
Bridges, Laurie M., Kelly McElroy and Zach Welhouse. 2024. “Generative Artificial Intelligence: 8 Critical Questions for Libraries.” Journal of Library Administration 64 (1): 66-79. https://doi.org/10.1080/01930826.2024.2292484.
“In this article, we provide a brief overview of generative artificial intelligence (GenAI) and large language models (LLMs). We then propose eight critical questions that libraries should ask when exploring this technology and its implications for their communities. We argue that libraries have a unique role in facilitating informed and responsible use of GenAI, as well as safeguarding and promoting the values of access, privacy, and intellectual freedom.” [Abstract]
Chen, Suzhen and Mingyan Li. 2024. “AI for Cataloging and Metadata Creation: Perspectives and Future Opportunities from Cataloging and Metadata Professionals.” Technical Services Quarterly 41 (4): 317-332. https://doi.org/10.1080/07317131.2024.2394919.
“This study examined the role of artificial intelligence (AI) in the field of cataloging and metadata creation through a case study approach. It explored the perceptions of cataloging and metadata professionals regarding the application and effectiveness of AI in their job duties, as well as the main challenges they encountered when utilizing AI for cataloging purposes...” [Abstract]
Cox, Andrew M. and Suvodeep Mazumdar. 2024. “Defining artificial intelligence for librarians.” Journal of Library and Information Science 56 (2): 330-340. https://doi.org/10.1177/09610006221142029.
“The aim of the paper is to define Artificial Intelligence (AI) for librarians by examining general definitions of AI, analysing the umbrella of technologies that make up AI, defining types of use case by area of library operation, and then reflecting on the implications for the profession, including from an equality, diversity and inclusion perspective.” [Abstract]
Elings, Mary, Marissa Friedman and Vijay Singh. 2024. “Using AI/Machine Learning to Extract Data from Japanese American Confinement Records.” Journal of eScience Librarianship 13 (1): e850. https://doi.org/10.7191/jeslib.850.
“The library utilized AI/machine learning to automate text extraction from over 220,000 images of a structured “standardized” form; our goal was to improve upon and collect information not previously recorded in the Japanese American Internee Data file held by the National Archives and Records Administration.” [Abstract]
Liu, Yifan, Peter Sullivan and Luanne Sinnamon. 2024. “AI Transparency in Academic Search Systems: An Initial Exploration.” Proceedings of the Association for Information Science and Technology 61 (1): 1002-1004. https://doi.org/10.1002/pra2.1167.
“As AI-enhanced academic search systems become increasingly popular among researchers, investigating their AI transparency is crucial to ensure trust in the search outcomes, as well as the reliability and integrity of scholarly work. This study employs a qualitative content analysis approach to examine the websites of a sample of 10 AI-enhanced academic search systems identified through university library guides.” [Abstract]
McIrvin, Caleb, Chreston Miller, Dina Smith-Glaviana and Wen Nie Ng. 2024. “Automatic Expansion of Metadata Standards for Historic Costume Collections.” Journal of eScience Librarianship 13 (1): e845. https://doi.org/10.7191/jeslib.845.
“This project focuses on Artificial Intelligence (AI) supported enhancement of descriptive metadata for fashion collections (otherwise known as costume or dress and textile collections) through expanding costume-specific controlled terms. The authors use Natural Language Processing (NLP) techniques along with a human-in-the-loop process to support selection of descriptive terms for inclusion in the controlled terms of a metadata schema.” [Abstract]
Monyela, Madireng and Adeyonka Tella. 2024. “Leveraging artificial intelligence for sustainable knowledge organization in academic libraries.” South African Journal of Libraries and Information Science 90 (2): 1-11. https://doi.org/10.7553/90-2-2396.
“This study explored the role of artificial intelligence (AI) in enhancing knowledge organisational practices within academic libraries, focusing on promoting sustainability in information management. The research investigated the potential of AI-driven tools and technologies to optimise resource utilisation, improve user experiences and contribute to environmentally conscious library practices.” [Abstract]
Taniguchi, Shoichi. 2024. “Creating and Evaluating MARC 21 Bibliographic Records Using ChatGPT.” Cataloging & Classification Quarterly 62 (5): 527-546. https://doi.org/10.1080/01639374.2024.2394513.
“This study investigated the feasibility of using ChatGPT (GPT-4) to create MARC 21 bibliographic records following RDA: Resource Description and Access. This study provided ChatGPT with data from information sources and evaluated its performance in referencing proper records, accurately interpreting information, and correctly applying RDA.” [Abstract]
Teel, Zoë (Abbie). 2024. “Artificial Intelligence’s Role in Digitally Preserving Historic Archives.” Preservation, Digital Technology & Culture 53 (1): 29-33. https://doi.org/10.1515/pdtc-2023-0050.
“This paper delves into the historical evolution of preservation methods driven by technological advancements as, throughout history, libraries, archives, and museums have grappled with the challenge of preserving historical collections, while many of the traditional preservation methods are costly and involve a lot of manual (human) effort.” [Abstract]
York, Elizabeth, David Hanegbi and Tamar Ganor. 2024. “Enriching Bibliographic Records Using AI - A Pilot by Ex Libris.” Internet Reference Services Quarterly 28 (3): 287-291. https://doi.org/10.1080/10875301.2024.2361871.
“This column introduces how Ex Libris uses ChatGPT to enrich ebooks’ Bibliographic records with the focus on these MARC fields: 041 (Language), 520 (Summary), and 650 (Subject).” [Abstract]