“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise
| DOI | https://doi.org/10.1108/JD-08-2022-0170 |
| Published date | 21 October 2022 |
| Date | 21 October 2022 |
| Pages | 12-29 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Amber L. Cushing,Giulia Osti |
“So how do we balance all of these
needs?”: how the concept
of AI technology impacts digital
archival expertise
Amber L. Cushing and Giulia Osti
School of Information and Communication Studies, University College Dublin,
Dublin, Ireland
Abstract
Purpose –This study aims to explore the implementation of artificial intelligence (AI) in archival practice by
presenting the thoughts and opinions of working archival practitioners. It contributes tothe extant literature
with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the
perceptions of digital archival expertise.
Design/methodology/approach –In this study a two-phase data collection consisting of four online focus
groups was held to gather the opinions of international archives and digital preservation professionals (n516),
that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template
analysis, a style of thematic analysis.
Findings –Four main themes were identified:fitting AI into day to day practice; the responsibleuse of (AI)
technology; managing expectations (about AI adoption) and bias associatedwith the use of AI. The analysis
suggeststhat AI adoption combined with hindsightabout digitisation as a disruptive technologymight provide
archival practitionerswith a framework for re-defining, advocating and outliningdigital archival expertise.
Research limitations/implications –The volunteer basis of this study meant that the sample was not
representative or generalisable.
Originality/value –Although the results of this research are not generalisable, they shed light on the
challenges prospected by the implementation of AI in the archives and for the digital curation professionals
dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved
for future research.
Keywords Focus groups, Qualitative, Archives, Digital preservation, Template analysis, Artificial
Intelligence (AI)
Paper type Research paper
JD
79,7
12
© Amber L. Cushing and GiuliaOsti. Published by Emerald Publishing Limited. This article is published
under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute,
translate and create derivative works of this article (for both commercial and non-commercial purposes),
subject to full attribution to the original publication and authors. The full terms of this licence may be
seen at http://creativecommons.org/licences/by/4.0/legalcode
This work was conducted with the financial support of the Science Foundation Ireland Centre for
Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224.
The authors would also like to thank Stephen Howell of Microsoft Ireland for his support with using
Microsoft Azure to request tags and descriptions for the phase one focus group prompts.
In addition, the authors would like to thank the following students who assisted with data collection
in the study: Rachael Agnew, MacKenzie Barry, Nancy Bruseker, Sinead Carey, Emma, Carroll, Lauren
Caravati, Na Chen, Caroline Crowther, Aoife Cummins Georghiou, Marc Dagohoy, Desree Efamaui,
Haichuan Feng, Laura Finucane, Nathan Fitzmaurice, Conor Greene, Yazhou He, Yuhan Jiang, Joang,
Zhou, Grainne Kavanagh, Kate Keane, Mark Keleghan, Miao Li, Danyang Liu, Xijia Liu, Siqi Liu,
Hannah Lynch, Conor Murphy, Niamh Elizabeth Murphy, Rebecca Murphy, Kyanna Murray, Kayse
Nation, Blaithin NiChathain, Roisin O’Brien, Niall O’Flynn, Abigail Raebig, Bernadette Ryan, Emma
Rothwell, John Francis Sharpe, Lin Shuhua, Zhongqian Wang, Robin Wharton, Zhillin Wei, India Wood,
Bingye Wu, Deyan Zhang, Zhongwen Zheng and Zheyuan Zhang.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 3 August 2022
Revised 26 September 2022
Accepted 29 September 2022
Journal of Documentation
Vol. 79 No. 7, 2023
pp. 12-29
Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-08-2022-0170
Introduction
Explorations ofthe use of artificialintelligence(AI) toolshave appearedin archival studiesin the
past few years. However, many of these articles are mostly limited to testing implementations
or opinion pieces from academia. Considering this landscape, we wish to expand the discussion
of AI technology in archives by empirically exploring the thoughts and opinions of archival and
digital preservation practitioners.
This article attempts to fill the gap by reporting on focus groups with those working in the
archives sector as practitioners about their thoughts and opinions related to adopting AItools
in archival work. Our goal is to situate the current discussion about using AI in archival
practice via the perspective of working archivists.
In doing so, we hope to learn more about the challenges that may exist in a potential wide-
spread implementation of AI technology in the archives field.
We aim to empirically explore potential social issues associated with the use of AI tools in
archival work as perceived by these practitioners, rather than focus on the outcome of a
specific application of the technology. We hope that this focus will add to the conversation
about AI in archives at the current time.
Literature review
Defining AI
Prevailingdiscussions about “Al and archives”aremediated by the definitionof “AI”currently
being used in the discussions. The existing discussions tend to favour social and cultural
definitions of AI over technical definitions that may be used in other fields. The highly cited
Crawford(2021) explains that thedefinition of AI shifts overtime.While “AI”is frequentlyused
in funding applications, the term “machinelearning”(ML) is more frequently used in technical
literature. She explains that ML can be understoodas a model that can learn from data it has
been given. This model can utilise ML and/or computer vision (CV). While ML focuses on
numerical,categorical,textual and temporal (timeseries) data, CV utilisesvisual data. Crawford
(2021) utilises the term ML to refer to technical approaches suchas broad scale data mining,
classificationof data andCV. The author uses the metaphorof an Atlas to describeAI due to the
technology’s far-reaching social and infrastructural implications.
Explicit definitions of AI in the context of archives have been offered by a few pieces
situated in archival studies research. In their survey of archival literature, Colavizza et al.
(2022) explain that they utilise the term AI as a proxy for ML, but also use AI “to encompass
the professional, cultural and social consequences of automated systems for recordkeeping
processes and for archivists”(p. 4). Also situated in archives and recordkeeping, Rolan et al.
(2019), references text from Bellman (1978) in their definition: “we understand AI as involving
digital systems that automate or assist in activities that we associate with human thinking,
activities such as decision-making, problem-solving, learning [and] creating”(p. 181). In this
study, we adopt Rolan et al.’s definition of AI.
Access and use
Viewing digital humanists as users of archival collections can also yield insight into existing
thoughts and opinions about archives and AI from the digital humanities (DH) perspective.
This perspective can be considered parallel to archival studies discussions about AI, as it is
written from the perspective of access and use and generally does not consider the work
practices of archival practitioners. Jaillant and Caputo (2022) express frustration at the
inability of archival repositories to make large digital datasets available in a timely manner.
Additionally, Jaillant (2022) noted that the lack of accessibility may impact end users –DH
practitioners included.
AI technology
and digital
archival
expertise
13
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