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

arXiv: https://arxiv.org/abs/2105.01117 · HTML: https://mlsanity.com/paper/2105.01117/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2021.

## Built on by (most cited first, 9 of 9)

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- [AI in Archival Science -- A Systematic Review](https://mlsanity.com/paper/2410.09086/) (2024)
- [ArchiveGPT: A human-centered evaluation of using a vision language model for image cataloguing](https://mlsanity.com/paper/2507.07551/) (2025)
- [The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique](https://mlsanity.com/paper/2212.11146/) (2022)
- [SIMARA: a database for key-value information extraction from full pages](https://mlsanity.com/paper/2304.13606/) (2023)
- [Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries](https://mlsanity.com/paper/2409.14181/) (2024)
- [Collective Memory and Narrative Cohesion: A Computational Study of Palestinian Refugee Oral Histories in Lebanon](https://mlsanity.com/paper/2501.13682/) (2025)
- [A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering](https://mlsanity.com/paper/2603.15722/) (2026)

## Abstract (arXiv)

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 which power modern artificial intelligence, and a more structural, yet critically-aware, integration of artificial intelligence into archival systems and practice.