Are Users of Digital Archives Ready for the AI Era? Obstacles to the Application of Computational Research Methods and New Opportunities

Author:

Jaillant Lise1ORCID,Aske Katherine1ORCID

Affiliation:

1. Loughborough University, United Kingdom

Abstract

Innovative technologies are improving the accessibility, preservation, and searchability of born-digital and digitised records. In particular, Artificial Intelligence is opening new opportunities for archivists and researchers. However, the experience of scholars (particularly humanities scholars) and other users remain understudied. This article asks how and why researchers and general users are, or are not, using computational methods. This research is informed by an open-call survey, completed by 22 individuals, and semi-structured interviews with 33 professionals, including archivists, librarians, digital humanists, literary scholars, historians, and computer scientists. Drawing on these results, this article offers an analysis of user experiences of computational research methods applied to digitised and born-digital archives. With a focus on humanities and social science researchers, this article also discusses users who resist this kind of research, perhaps because they lack the skills necessary to engage with these materials at scale, or because they prefer to use more traditional methods, such as close reading and historical analysis. Here, we explore the uses of computational and more “traditional” research methodologies applied to digital records. We also make a series of recommendations to elevate users’ computational skills but also to improve the digital infrastructure to make archives more accessible and usable.

Publisher

Association for Computing Machinery (ACM)

Reference80 articles.

1. Examples of born-digital records include emails PDFs Word documents audio and video digital files. These born-digital records differ from “digitised” materials that originated in paper form and were remediated thanks to processes such as scanning and photographing.

2. Emily Maemura, Christoph Becker, and Ian Milligan. 2016. Understanding computational web archives research methods using research objects. In Proceedings of the IEEE International Conference on Big Data (Big Data'16). 3250--3259.

3. Methods and Approaches to Using Web Archives in Computational Communication Research

4. See the project website. https://www.aeolian-network.net

5. Artificial intelligence (AI) refers to the use of computational processes to learn make decision and solve problems. Machine learning (ML) which is often referenced in discussions about AI is an application of it. ML is the process by which a computer system is able to continue learning and improving on its own based on previous processes it has undertaken. ML is considered a subset of AI.

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