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.
Highlights
Long before big data as an idea had been invented, archives already measured their collections in kilometers of files and folders
Jo [2019] turns to the standards for archival selection and description that have been developed over centuries of recordkeeping and explores their potential as a model for curating less-biased, more-transparent, and inclusive socio-cultural training data for machine learning
Our contribution lies in the methodology and structure we have given the work that has been surveyed, by framing it within the Records Continuum model
Summary
Long before big data as an idea had been invented, archives already measured their collections in kilometers of files and folders. Ten years ago AI activities in archives were still largely experiments that showcased a potential, as they offered new ways of working with specific parts of the archival holdings like digitised newspaper collections. The authors start from the same assumption that archival practice will be transformed by new advanced digital methodologies such as machine learning They go through a range of case studies to describe a new interdiscipline at the intersection of archival and computer science and make detailed suggestions for changes to archival education. We critically discuss these trends and underline what future opportunities we see for this area of study
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