The intelligent library. Thought leaders’ views on the likely impact of artificial intelligence on academic libraries
| DOI | https://doi.org/10.1108/LHT-08-2018-0105 |
| Pages | 418-435 |
| Date | 16 September 2019 |
| Published date | 16 September 2019 |
| Author | Andrew M. Cox,Stephen Pinfield,Sophie Rutter |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
The intelligent library
Thought leaders’views on the likely impact
of artificial intelligence on academic libraries
Andrew M. Cox
Information School, University of Sheffield, Sheffield, UK
Stephen Pinfield
University of Sheffield, Sheffield, UK, and
Sophie Rutter
University of Leeds, Leeds, UK
Abstract
Purpose –The last few years have seen a surge of interest in artificial intelligence (AI). The purpose of this
paper is to capture a snapshot of perceptions of the potential impact of AI on academic libraries and to reflect
on its implications for library work.
Design/methodology/approach –The data for the study were interviews with 33 library directors, library
commentators and experts in education and publishing.
Findings –Interviewees identified impacts of AI on search and resource discovery, on scholarly publishing
and on learning. Challenges included libraries being left outside the focus of development, ethical concerns,
intelligibility of decisions and data quality. Some threat to jobs was perceived. A number of potential roles for
academic libraries were identified such as data acquisition and curation, AI tool acquisition and infrastructure
building, aiding user navigation and data literacy.
Originality/value –This is one of the first papers to examine current expectations around the impact
of AI on academic libraries. The authors propose the paradigm of the intelligent library to capture the
potential impact of AI for libraries.
Keywords Academic libraries, University libraries, Data mining, Artificial intelligence, Librarians,
Machine learning
Paper type Research paper
Introduction
Following several years of intense activity around big data, there has been a surge of
interest in AI. For example, in the UK, reports by the House of Commons Science and
Technology Committee (2016) on AI and by the Royal Society (2017) specifically on Machine
Learning have been followed by the publication of findings of a House of Lords Select
Committee on Artificial Intelligence (2018). Artificial intelligence (AI) has come into public
awareness through maturing consumer products that use voice recognition, such as
Siri, and high profile innovations, such as smart cars (Tredinnick, 2017). Political interest in
AI, motivated by its potential to raise productivity and stimulate economic growth, has been
coupled with societal “AI anxiety”about the impact on jobs and social equality, and
with a growing awareness of the risks to privacy ( Johnson and Verdicchio, 2017). A Price
Waterhouse Cooper report of 2017 found 54 per cent of senior executives were planning to
make major strategic investment in AI, but most thought their organisation currently lacked
relevant skills (quoted Rao, 2017) pointing to another important issue, one echoed by
Gartner (Andrews and Austin, 2018).
AI has a long history of development, but it seems to be on the cusp of a breakthrough in
application. Some information sectors such as law are already starting to see a significant
impact (Smith, 2016; Chen and Neary, 2017). The likely effects on academic libraries are
unclear, however. In some senses, AI has already had effects here, e.g. changes to search and
discovery, experiments with chatbots and work supporting Text and Data Mining; though
Library Hi Tech
Vol. 37 No. 3, 2019
pp. 418-435
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-08-2018-0105
Received 11 August 2018
Accepted 25 September 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
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these are rarely understood as interconnected changes. That there will be further impacts
on libraries seems inevitable. Fernandez (2016, p. 22), for instance, goes as far as to say,
“For libraries the question is not so much what technology will be affected, but rather what
technology, if any, will remain unaffected by AI”.
In a 2017 survey of librarians from acrossall sectors in the USA, Wood and Evans (2018)
found that 56.3 per cent of respondents thought supercomputers, like Watson, could
transform librarianship. This still meant that 44 per cent thought it would have no or not
much effect.Furthermore, most thought it wouldbe 30 years before supercomputers wouldbe
in libraries. The effect would be seen in virtual services, discovery, referencing and
cataloguing; other library functions would be less affected, respondents thought. Is that
correct? And thisleaves open the question of what mightbe the effects of other aspects ofAI,
beyond the model of Watson that Wood and Evans chose. Respondents saw the effect as
mostly positiveand not likely to involve the replacementof librarians or disintegration of the
library. Is thisoptimism justified, when widelycited studies on the impact of automationmore
generally are more pessimistic? In their seminal study, Frey and Osborne (2017) estimate
the probability of the replacement by computers of “library technicians”as 99 per cent,
“Library assistants, clerical”95 per cent, archivists 76 per cent and librarians 65 per cent.
In this context, the current paper seeks to capture a snapshot of views in 2017 on the
potential impact of AI on academic libraries and to reflect on its implications for library
work, based on interviews with 33 library directors, library thought leaders and experts
from related areas.
Artificial intelligence
Definition and scope
AI has long been an important area of research in computing. There have been previous
spurts of development, e.g., in the 1980s followed by “AI winters”(House of Lords Select
Committee on Artificial Intelligence, 2018). But towards the end of the current decade it
seems that AI is “entering a crucial stage in its development and adoption”(House of Lords,
2018, p. 15). AI is not a unitary concept, however, it is usual to differentiate general or strong
AI (aspiring to match the general intelligence of a human being) from narrow or weak
AI where applications work on a particular problem space. It is in the latter where current
development is happening.
Tredinnick (2017, p. 37) defines AI as “a cluster of technologies and approaches to
computing focussed on the ability of computers to make flexible rational decisions in
response to unpredictable environmental conditions”. Hare and Andrews (2017) define it as
“systems that change behaviours without being explicitly programmed based on data
collected, usage analysis and other observations”. It is a trend linking “process automation”,
the “Internet of Things”,“Data processing”,“tangible robotics”,“conversational interaction”
and “decision support”, they suggest. Smith (2016, p. 221), writing about the impact in the
legal sector, sees the “AI bucket”somewhat more narrowly as consisting of:
•big data;
•analytics;
•machine learning;
•natural language processing (NLP);
•data visualisation; and
•decision logic.
Thus, the hype around AI, builds on the hype around big data in the last few years,
for it is the combination of masses of data with computing power that creates the potential
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Intelligent
library
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