GLOW Scoping Study
Sifting the Digital Heap: A scoping study of AI for government archives – access, backlogs, and responsible practice
AI can play a decisive role in making digital government records more accessible and manageable, provided that its use is grounded in responsibility and clear purpose. Work is already underway across archives and government, where AI is being used to manage scale, improve accuracy, and enhance public access to digital records – including email, PDFs, spreadsheets, images, scanned documents, audiovisual assets, and social media posts. Building on these foundations, the GLOW study identifies four interlinked priorities for responsible and effective adoption.
The report highlights four priorities for the responsible use of AI in government archives:
_Unlock records with AI-powered tools;
_Adapt to users’ expectations;
_Implement a framework for responsible AI;
_Establish a coordinated strategy across institutions.
Citation
Jaillant, L., Kidd, M., & Zhao, L. (2026). Sifting the Digital Heap: A scoping study of AI for government archives – access, backlogs, and responsible practice. Zenodo. https://doi.org/10.5281/zenodo.18935870
Special Issue
Special Issue on When Data turns into Archives: Making Digital Records More Accessible with AI , in AI and Society
The overall aim of this special issue is to explore how AI can help improve the preservation, access and usability of digital and born-digital archives. It focuses on the perspective and the challenges that AI can offer in unlocking archival data in various sectors (including government).
Bringing together digital humanists and social scientists, AI experts, professionals in Information Management, archivists, librarians, and museum professionals, this special issue welcomes contributions that explore themes including, but not limited to:
_AI applied to archival data created by government, cultural heritage organizations or other institutions;
_“Digital Heap” and the issue of disorganized data;
_Making archival data more accessible for public good;
_Risks associated with AI applied to born-digital records;
_Mitigating these risks: AI and ethics /Designing responsible AI systems;
_Research methods (including AI approaches) to use archival data;
_Qualitative approaches, for example to survey professional attitudes towards AI and archives.
ARTICLES PUBLISHED IN THIS SPECIAL ISSUE (AI & Society 2025)
Jaillant, L., Zhao, L. Introduction: When data turns into archives: making digital records more accessible with AI.
Baron, J.R. Using AI in providing greater access to the U.S. government’s email: a progress report.
Coleman, C.N., Liu, J., Williams, C.K. Can AI help make California police policy human centered?
Toth, G.M., Albrecht, R., Pruski, C. Explainable AI, LLM, and digitized archival cultural heritage: a case study of the Grand Ducal Archive of the Medici.
Taurino, G., Sweeney, S., Facklam, D., Smith, D.A. Copyright, Privacy, and Public Access in News Archives: a proof of concept on the Boston Globe photograph morgue.
Nix, A., Decker, S., Kirsch, D.A. Conceptualising methodological diversity among born-digital users: insights from the garbage can model.
Reusens, M., Adams, A. & Baesens, B. Large Language Models to make museum archive collections more accessible.
Jansen, G., Marciano, R. Developing computer vision and machine learning strategies to unlock government-created records.
Jaillant, L., Mitchell, O., Ewoh-Opu, E. et al. How can we improve the diversity of archival collections with AI? Opportunities, risks, and solutions.
Liu, Y., Heitman, C., Soh, LK. et al. Machine learning methods for isolating indigenous language catalog descriptions.
Canning, D., Jaillant, L. AI to review government records: new work to unlock historically significant digital records.
Vetter, M.A., Jiang, J. & McDowell, Z.J. An endangered species: how LLMs threaten Wikipedia’s sustainability.
McKean, C., Randall, C. Data analysis and network visualisation as tools for curating hybrid correspondence archives.
Green, P. AI and the visualisation needs of researchers using email archives.
Arias Hernández, R., Rockembach, M. Building trustworthy AI solutions: integrating artificial intelligence literacy into records management and archival systems.
The special issue is a key research output of the LUSTRE project funded by the Arts and Humanities Research Council (AHRC) in the UK.