Archives and AI: An Overview of Current Debates and Future Perspectives

Abstract

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

“…Chabin (2020) uses case study to highlight a point also found by Bunn (2020) and Colavizza et al. (2022) – that other archival and recordkeeping principles may be of use to the AI field, specifically authenticity.…”

“…Other scholars have also highlighted the colonialist overtones of provenance and original order, explaining that they can conflict with diversity and inclusion goals (Punzalan and Caswell, 2016;Steinmeier, 2020). Chabin (2020) uses case study to highlight a point also found by Bunn (2020) and Colavizza (2022)-that other archival and recordkeeping principles may be of use to the AI field, specifically authenticity. Chabin (2019) uses the case of the data processing associated with the great debate in France to explain how diplomatic analysis can be used to enrich a dataset that is used in ML or CV.…”

“…Participants expressed concern about this specific issue-how AI technology may inappropriately be applied by users in the context of marginalised and underrepresented groups represented in the collections for which they care. Much of the exiting archives literature about AI technology is focused on how AI may impact the archives field: this includes how AI may change traditional archival work such as arrangement, description and appraisal via automation (Lee, 2018); how traditional archival theory such as original order and provenance may change (Colavizza et al, 2022); and how AI technology will require archivists to learn new computational skills (Marciano et al, 2018;Payne, 2018). Moss et al (2018) and Theimer (2018) specifically address the concept of archival expertise and how it may change in the context of AI, suggesting that expertise would grow to closely resemble data science.…”

“…Explicit definitions of AI in the context of archives have been offered by a few pieces situated in archival studies research. In their survey of archival literature, Colavizza et al. (2022) explain that they utilise the term AI as a proxy for ML, but also use AI “to encompass the professional, cultural and social consequences of automated systems for recordkeeping processes and for archivists” (p. 4).…”

parts and the whole. Integrate knowledge

“…Ci vuole magari l'accortezza di non lasciarsi travolgere dal tecnicamente possibile, per evitare che le fascinazioni digitali spacchino in due la realtà, ma detto questo è chiaro che le filosofie alla base del machine learning hanno ormai un loro senso profondo in questo contesto e le reti neuronali dei computer non sembrano più così disumane. Machine learning e deep learning non sono fantascienza ma pratica quotidiana e le intelligenze artificiali ci chiedono a gran voce di essere utilizzate (Colavizza et al 2021).…”