Finn, Finola and Khosrowi, Donal (2025) AI assistants in the archive and the lure of ‘instant history’. [Preprint]

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Abstract

AI assistants are increasingly used for navigating and analysing the contents of major archives. Applying Retrieval Augmented Generation to existing large language models, these tools draw on indexes of the relevant archives to answer, in natural language, users’ questions. In addition to being powerful finding aids, archival AI assistants are also presented as being capable of providing useful, automated answers to questions about the past. This article argues that such tools and how they are marketed result in major conceptual disruptions and uncertainties, placing pressure on our understanding of a range of roles, forms of information, and outputs involved in the production of historical knowledge. In particular, we argue that these tools may obscure well-established beliefs that ‘sources’ and ‘archives’ are not unmediated, clearly navigable, or necessarily comprehensive, and that the processes by which these are used to write ‘history’ are by no means straightforward or instantaneous. With the aim of mitigating these misunderstandings, the article makes suggestions for how deployers could more carefully frame and describe the intended use of archival AI assistants (especially for public users), to ensure that their benefits for accessibility are exploited while also avoiding misconceptions and safeguarding rigorous historical practice.

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Item Type:	Preprint
Creators:	Creators Email ORCID Finn, Finola finola.finn@uni.lu 0009-0000-1805-0442 Khosrowi, Donal donal.khosrowi@philos.uni-hannover.de 0000-0002-9927-2000
Keywords:	Archival AI assistants, Historical method, Responsible AI, Digital history, Large Language Model chatbots, Retrieval-Augmented Generation, Conceptual disruptions
Subjects:	Specific Sciences > Artificial Intelligence Specific Sciences > Historical Sciences Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Social Epistemology of Science General Issues > Technology
Depositing User:	Finola Finn
Date Deposited:	13 Oct 2025 17:09
Last Modified:	13 Oct 2025 17:09
Item ID:	26886
Subjects:	Specific Sciences > Artificial Intelligence Specific Sciences > Historical Sciences Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Social Epistemology of Science General Issues > Technology
Date:	1 August 2025
URI:	https://philsci-archive.pitt.edu/id/eprint/26886

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