# AI in Archival Science -- A Systematic Review

arXiv: https://arxiv.org/abs/2410.09086 · HTML: https://mlsanity.com/paper/2410.09086/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Archives and AI: An Overview of Current Debates and Future Perspectives](https://mlsanity.com/paper/2105.01117/) (2021)
- [Text Analysis Using Deep Neural Networks in Digital Humanities and Information Science](https://mlsanity.com/paper/2307.16217/) (2023)
- [Providing More Efficient Access To Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege](https://mlsanity.com/paper/2011.07203/) (2020)
- [Constructing Image-Text Pair Dataset from Books](https://mlsanity.com/paper/2310.01936/) (2023)
- [Write What You Want: Applying Text-to-video Retrieval to Audiovisual Archives](https://mlsanity.com/paper/2310.05825/) (2023)
- [Leveraging Machine Learning to Detect Data Curation Activities](https://mlsanity.com/paper/2105.00030/) (2021)

## Built on by (most cited first, 2 of 2)

- [Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index](https://mlsanity.com/paper/2508.19093/) (2025)
- [Catalogue Grounded Multimodal Attribution for Museum Video under Resource and Regulatory Constraints](https://mlsanity.com/paper/2603.11147/) (2026)

## Abstract (arXiv)

The rapid expansion of records creates significant challenges in management, including retention and disposition, appraisal, and organization. Our study underscores the benefits of integrating artificial intelligence (AI) within the broad realm of archival science. In this work, we start by performing a thorough analysis to understand the current use of AI in this area and identify the techniques employed to address challenges. Subsequently, we document the results of our review according to specific criteria. Our findings highlight key AI driven strategies that promise to streamline record-keeping processes and enhance data retrieval efficiency. We also demonstrate our review process to ensure transparency regarding our methodology. Furthermore, this review not only outlines the current state of AI in archival science and records management but also lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced collaboration between the disciplines of artificial intelligence and archival science.