Collections: A Journal for Museum and Archives Professionals

The presence of artificial intelligence (AI) in academic and professional work is becoming increasingly prevalent. AI is no longer a technology that GLAM practitioners can ignore or disregard. Practitioners must be at the heart of considerations regarding AI integration into GLAM institutions to safeguard the profession’s core values of equity, fair access, intellectual freedom, and social responsibility. This thematic review encompasses professional publications and peer-reviewed articles. It serves as a “syllabus of understanding” that will empower GLAM practitioners, students, and emerging professionals to assess and define ethical stewardship in the time of AI integration.

This article equips readers with processes to identify ethical artificial intelligence solutions that may benefit libraries, archives, and museums. It provides definitions of common artificial intelligence concepts and terms. It then describes existing and potential use cases for artificial intelligence solutions. It concludes with an in-depth discussion on how to interrogate artificial intelligence models for potential ethical issues and how to remediate those issues before implementation.

Current research into AI tools in archives has primarily focused on their utility in technical services and supporting collections management. Studies on using AI in search systems have largely focused on digital materials. However, archival reference practices have largely been developed with the assumption of guiding users to analog collections. Simultaneously, researchers are increasingly exploring AI tools for research and archival discovery and may be relying on chatbot suggestions without ever consulting archivists’ descriptions directly. Archivists, we believe, have a responsibility to understand how AI tools change reference interactions. We explore the ethical implications and archivists’ central role in continuing to support researchers as they develop “archival intelligence” in the context of generative AI.

This article considers “flow theory” as both a basic scaffolding for AI literacy initiatives in Library and Information Science (LIS) education and a workplace strategy that can empower practitioners in galleries, libraries, archives, and museums (GLAM) to prioritize human-centered agency in the development of AI-enhanced stewardship practices. In contrast to one-off workshops or general AI classes, this article explores how “learning in the flow” promotes an approach to AI literacy that centers continuous, embedded microskilling with responsible stewardship practices. With practitioner-focused, values-aligned literacy frameworks, GLAM practitioners can navigate AI adoption against the backdrop of collection care practices, institutional values, and professional codes of ethics. This approach to AI literacy aligns digital transformation, ethical stewardship, and values-based, context-aware learning with those closest to the work of collections stewardship. Finally, this article explores how microskilling and an ethics of care can be integrated into hybrid skill development paths that reflect the complexity of the ever-evolving AI training needs of GLAM practitioners.

This paper examines optical character recognition (OCR) through the lens of archival ethics as outlined in the Society of American Archivists (SAA) Core Values Statement and Code of Ethics, given the current debates surrounding artificial intelligence (AI). A literature review highlights persistent challenges of authenticity and integrity, transparency and accountability, access and equity, and responsible stewardship and sustainability, as well as new concerns about bias, sustainability, and accountability using large language models (LLM). A case study describes systematic testing of LLM, transformer model (TM), and neural network (NN) architectures and examines the challenges in creating a reliable, scalable in-house OCR tool named Opticolumn. This case study finds that NN approaches better align with archival ethics than do LLM tools, which may generate fabrications, but that OCR tool choice will depend on the capacities and preferences of individual institutions.

This article is a scholarly reflection on using the AI tools Transkribus and AntConc as part of a digital humanities PhD Placement within the British Library to extract metadata from printed catalogues for the online catalogue. This project focuses on BMC XI, the catalogue of English Incunabula at the British Library published in 2007. Transkribus is a “comprehensive platform for the digitization, AI-powered text recognition, transcription, and searching of historical documents” while AntConc is a “freeware corpus analysis toolkit for concordancing and text analysis”. Together, these tools can be used to extract information en masse to be uploaded to the specialist databases MEI (Material Evidence Incunabula) and the ISTC (Incunabula Short Title Catalogue) as well as pick out patterns and trends within incunabula descriptions. This project followed FRAIM (Framing responsible AI implementation and management) principles.

Descriptive metadata is essential for discovery in audiovisual archives, yet many collections remain minimally described due to limited resources. Beginning in 2024, the University at Buffalo, University Archives launched an ongoing pilot project exploring the use of consumer-level generative AI tools to streamline audio description workflows. Archivists tested ChatGPT and Copilot on transcripts from over 2,000 hours of institutional radio content, using A/B prompt testing to refine outputs and generate concise summaries resembling traditional archival descriptions. The project prioritized free or low-cost AI tools that could be readily adopted by archives with varying resources. To date, 1,230 programs have been described using this approach. While achieving significant efficiency gains, the project also raised ethical questions around privacy, copyright, and professional practice. The pilot demonstrates the potential for AI-assisted description to enhance discovery while highlighting the need for ongoing evaluation of ethical and practical implications in archival work.