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

TLDR

Digital transformation is turning archives into data, prompting AI automation to scale recordkeeping, capture, organization, and access. This paper surveys recent developments at the intersection of artificial intelligence and archival thinking and practice. The overview is organized through the Records Continuum model, framing the literature across the lifecycle of records. The literature reveals four broad themes—theoretical and professional considerations, automation of recordkeeping processes, organization and access of archives, and novel digital archives—and highlights emerging trends toward applying recordkeeping principles to AI data and integrating AI structurally into archival systems.

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.