Speaker: Dr Daniel Chávez Heras, King’s College London
Abstract: When archives purchase AI services, they receive outputs—automated transcription, entity extraction, metadata generation—but the knowledge of how those outputs were produced remains with the vendor. Each token bought is capacity not built. This transactional model offers efficiency but forecloses institutional learning: archives become consumers of AI rather than participants in its development, and the sector’s collective understanding of what these technologies can and cannot do remains shallow.
The Intelligent Systems for Screen Archives (ISSA) project, funded by the BFI through the National Lottery, explores an alternative path. Five UK regional and national moving image archives are collaborating with King’s College London to develop open-source, modular prototypes for metadata enrichment, collection visualisation, and retrieval. The investment is slower and harder than direct procurement, but the returns are different in kind: shared infrastructure, transferable methods, and crucially, collective knowledge about AI’s capabilities and limitations in archival contexts.
ISSA’s approach alternates between engagement and development: fifteen interviews with archive professionals were distilled into four use cases and technical requirements, followed by iterative prototyping toward minimum viable products, a demonstrator event with all partners, and upcoming situated workshops that will apply these tools to specific archival challenges across the UK. This structure is designed not only to produce functional tools, but to generate and circulate practical knowledge across institutions with different scales, capacities, and priorities.
This presentation reflects on early lessons from ISSA’s first phase and shares the project’s direction of travel. While ISSA focuses on moving image collections, the underlying questions—about ownership, capacity-building, and the distribution of knowledge in public AI development—are shared across the GLAM sectors and resonate directly with the challenges facing government archives as they navigate AI adoption. The goal is not to offer a model to replicate, but to contribute one example to a broader conversation about what public investment in AI research and development might look like.
Bio: Daniel Chávez Heras is Lecturer in Digital Culture and Creative Computing in the Department of Digital Humanities at King’s College London, and Principal Investigator of the ISSA project. His research combines critical frameworks from film, television, and media studies with technical practice in creative and scientific computing, including applied machine learning. He works with cultural institutions including the British Film Institute, the British Council, and the BBC, and is the author of Cinema and Machine Vision: Artificial Intelligence, Aesthetics and Spectatorship (Edinburgh University Press, 2024).