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AI to review government records: New work to unlock historically significant digital records

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journal contribution
posted on 2025-02-27, 14:02 authored by David Canning, Lise JaillantLise Jaillant
This article presents ground-breaking new work on AI applied to the government records of the Cabinet Office, in order to automatically identify historically significant records to preserve and other records that can be deleted. The Cabinet Office is the UK central government department that supports the Prime Minister in the effective running of government. Its records are among the most important that are deposited in The National Archives, covering those of the Prime Minister, Cabinet proceedings, government efficiency and reform, and the formulation of legislation, among other areas. This article does not only describe a radically new methodology to appraise digital records, it also makes a significant theoretical contribution to academic disciplines that seldom talk to each other – including archival studies, digital humanities and computer/ data science. It proposes a new archival mindset that reflects the digital realities faced by records managers, archivists and users. This theoretical approach combines distant viewing (necessary to approach huge amounts of records) and close reading of a selection of records (to ensure the effectiveness of computational approaches). This combination of “distant” and “close” is an answer to the need to move away from paper minds, and embrace digital archival realities and methodologies. The conclusion explores where records management might go next in deploying Artificial Intelligence technologies and the benefits these may have in opening up archives for future historians. Recommendations are offered to facilitate the ethical application of AI to records and archives, in the government sector and beyond.

Funding

Unlocking our Digital Past with Artificial Intelligence (LUSTRE)

Arts and Humanities Research Council

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History

School

  • Social Sciences and Humanities

Department

  • Communication and Media

Published in

AI and Society

Publisher

Springer

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Acceptance date

2025-02-03

Publication date

2025-02-22

Copyright date

2025

ISSN

0951-5666

eISSN

1435-5655

Language

  • en

Depositor

Prof Lise Jaillant. Deposit date: 12 February 2025