The Rise of AI:: Implications and Applications of Artificial Intelligence in Academic Libraries (Volume 78) (Publications in Librarianship)

221 Pages·2022·13.963 MB·English

by  Sandy Hervieux (editor), Amanda Wheatley (editor)

ACRL PUBLICATIONS IN LIBRARIANSHIP NO. 78
Rise AI
The of
Implications Applications
and
Artificial Intelligence
of
Academic Libraries
in
edited by
Sandy Hervieux and
Amanda Wheatley
Association of College and Research Libraries
A division of the American Library Association
Chicago, Illinois 2022
The paper used in this publication meets the minimum requirements of American
National Standard for Information Sciences–Permanence of Paper for Printed Library
Materials, ANSI Z39.48-1992. ∞
Library of Congress Control Number: 2022933641
Copyright ©2022 by the Association of College and Research Libraries.
All rights reserved except those which may be granted by Sections 107 and 108 of the
Copyright Revision Act of 1976.
Printed in the United States of America.
26 25 24 23 22 5 4 3 2 1
Contents
vii Acknowledgements
ix Introduction
PART I: USER SERVICES
3 Chapter 1
The 99 AI Challenge: Empowering a University Community
through an Open Learning Pilot
Carey Toane, Lise Doucette, Paulina Rousseau, Michael Serafin, Michelle
Spence, and Christina Kim
15 Chapter 2
URI Libraries’ AI Lab—Evolving to Meet the Needs of
Students and Research Communities
Harrison Dekker, Angelica G. Ferria, and Indrani Mandal
35 Chapter 3
Artificial Intelligence, Machine Translation, and Academic
Libraries: Improving Machine Translation Literacy on
Campus
Lynne Bowker, Maria Kalsatos, Amy Ruskin, and Jairo Buitrago Ciro
47 Chapter 4
Incubating AI: The Collaboratory at Ryerson University
Library
Fangmin Wang, Aaron Tucker, and Jae Duk Seo
iii
iv Contents
61 Chapter 5
Separating Artificial Intelligence from Science Fiction:
Creating an Academic Library Workshop Series on AI
Literacy
Amanda Wheatley and Sandy Hervieux
71 Chapter 6
Do Students Dream of Electric Cats (or Dogs)?: Using
Robotics for a Unique Exam Week Activity in the Library
Jonathan Scherger, Juliana Espinosa, Autumn Edwards, Chad Edwards, Bryan
Abendschein, and Patricia Vander Meer
PART II: COLLECTIONS AND DISCOVERY
83 Chapter 7
Subjectivity and Discoverability: An Exploration with
Images
Catherine Nicole Coleman, Claudia Engel, and Hilary Thorsen
95 Chapter 8
AI-Informed Approaches to Metadata Tagging for
Improved Resource Discovery
Charlie Harper, Anne Kumer, Shelby Stuart, and Evan Meszaros
111 Chapter 9
“We Could Program a ‘Bot’ to Do That!”: Robotic Process
Automation in Metadata Curation and Scholarship
Discoverability
Anna Milholland and Mike Maddalena
123 Chapter 10
More Than Just Algorithms: A Machine Learning Club for
Information Specialists
Mark Bell and Leontien Talboom
137 Chapter 11
The Role of the Library When Computers Can Read:
Critically Adopting Handwritten Text Recognition (HTR)
Technologies to Support Research
Melissa Terras
Contents v
149 Chapter 12
Using IBM Watson for Discovery and Research Support: A
Library-Industry Partnership at Auburn University
Aaron Trehub and Ali Krzton
PART III: TOWARD FUTURE APPLICATIONS
165 Chapter 13
Ethical Implications of Implicit Bias in AI: Impact for
Academic Libraries
Kim Paula Nayyer and Marcelo Rodriguez
175 Chapter 14
Machine Information Behaviour
Michael Ridley
189 Biographies
195 Index

Acknowledgments
The authors would like to acknowledge the support of the following institutions and
individuals during this project: the McGill University Library and our colleagues at the
Humanities and Social Sciences Library, Erin Nevius, and the Association of College and
Research Libraries. The editors also wish to thank the chapter authors for all their hard
work and their willingness to share their innovative ideas, projects, and initiatives. A
special thank you to our families for their unwavering support and encouragement: Sylvie
et Robert, Tammy, Anis, Melanie, and Laura.
vii

Introduction
Introduction
Artificial intelligence or AI, if you prefer, has been in the minds of researchers and dream-
ers alike for decades. It brings about connotations of science fiction and fantasy, but
perhaps more importantly, of progress. After all, AI and progress go hand in hand. So
where then does the academic library fit into the ever-growing expanse of artificial intel-
ligence? The answer to this question was posed over forty years ago as librarians began
to see the shift from automation to computer intelligence. Nearly half a century has been
spent trying to prepare for a hypothesized takeover of librarian jobs. This fear would seem
natural: librarians are information stewards, and some of the most brilliant technological
advancements in the last hundred years have sought to open information access to an
even broader public. The addition of AI to these advancements has made the information
seeking process nearly unrecognizable from previous generations.
So, what exactly is artificial intelligence? The editors of this book provide a living
definition that they believe stands true at this time; however, they recognize the fluidity
of the field and the ability for this definition to change over time. Ask them again ten
years after the publication of this book or even after a few months, if you prefer, and it
is likely that the changing landscape of AI will have brought on new considerations to
this definition. Nevertheless, for the purposes of this book, the editors define artificial
intelligence as the development of machines to accomplish tasks and reproduce thought
processes that are normally seen in humans; this simulation of intelligent behaviour is
unique from other automation as it requires the computer to use human reasoning or
thinking to perform tasks.
Along with AI, machine learning has quickly provided new solutions to information
professionals. Machine learning (ML) is acknowledged as a subset of AI, though some
scholars and scientists would argue it is its own field. The application of machine learning
begins with its ability to learn from data and make decisions without the express inter-
vention of a human. An ML algorithm can observe and detect patterns in data with the
goal of being able to predict future decisions and outcomes from said data. ML learns
as it goes and can adapt new stimuli into its decision-making process, much like that of
a human. The advancement of ML technologies opens the door for endless possibilities
for librarians to utilize these programs to classify, label, and organize information in an
automated process. Some of these possibilities will be explored within the chapters of
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