Table Of ContentTowards an International
Political Economy of
Artificial Intelligence
Edited by Tugrul Keskin · Ryan David Kiggins
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Tugrul Keskin Ryan David Kiggins
Editors
Towards
an International
Political Economy
of Artificial
Intelligence
Editors
Tugrul Keskin Ryan David Kiggins
Center for Global Governance Political Science
Shanghai University University of Central Oklahoma
Shanghai, China Edmond, OK, USA
ISSN 2662-2483 ISSN 2662-2491 (electronic)
International Political Economy Series
ISBN 978-3-030-74419-9 ISBN 978-3-030-74420-5 (eBook)
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Foreword
There is a great deal of excitement around artificial intelligence (AI) at
the start of the 2020s. Gone are the days when the subject was just the
preserve of computer scientists and phycologists. Suddenly everyone is
interested, including artists, authors, businesspeople, economists, engi-
neers, politicians, scientists, and social scientists. This blossoming in the
popularityofAIiswelcome,butweshouldbewarynottobelieveallthat
we hear or read about it.
A common misconception is that AI is new. Yet Alan Turing first
published the notion of computers that can think in 1950. In the same
article, he proposed the Imitation Game, now widely referred to as the
Turing Test, to evaluate whether a computer system displays intelligence.
If, in a natural-language typed conversation, you cannot tell whether you
are chatting with a computer or a person, then the computer has passed
the test.
Six years later, the Dartmouth Conference on AI was held at Dart-
mouthCollege,NH.Theproposalfortheconference,issuedin1955,was
the first published use of the phrase “artificial intelligence”. The confer-
ence itself was a loose gathering of experts who exchanged ideas during
the summer of 1956. During the decades that followed, many practical
and useful applications of AI were developed. They were mostly focused
onexplicitknowledge-basedrepresentationsofAI.ThisfamilyofAItech-
niques included so-called expert systems that captured expert knowledge
asaformofadvisoryassistantinspecialistdomainslikemedicaldiagnosis,
v
vi FOREWORD
spectrometry,mineralprospecting,andcomputerdesign.Theirpopularity
peaked in the 1980s, but they are still an important technique today.
Atthesametime,aseparatefamilyofAIresearchhadbeenfocusedon
models inspired by the neurons and interconnections of the brain. These
data-driven models of AI had been of only academic interest until 1985,
whenapracticalcombinationofanartificialneuralnetworkstructurewith
aneffectivelearningalgorithmwaspublished.(Specifically,theback-error
propagation algorithm was shown to train a multilayered perceptron.)
Thatbreakthroughledtoanotherwaveofexcitementaroundthesestruc-
tures that could effectively learn to classify data, guided by experience
from training examples.
So, what has happened to cause such excitement 35 years after that
breakthrough and 70 years after Turing’s article? I can think of five main
reasons. First, the two broad families of AI models have matured and
improved through iterative development. Second, there have been some
morerecentdevelopments,suchasdeep-learningalgorithms,whicharea
newer and more powerful type of neural network for machine learning.
Third, the rise of the Internet has enabled any online system to access
vastamountsofinformationandtodistributeintelligentbehaviorsamong
networked devices. Fourth, huge quantities of data are now available to
train an AI system. Finally, and perhaps most importantly, the power
of computer hardware has improved to the extent that many computa-
tionally demanding AI concepts are now practical on affordable desktop
computers and mobile devices.
Much of the current interest in AI is focused on machine learning.
The principle is simple: show a large artificial neural network thousands
ofexamplesofimagesorotherformsofdata,anditwilllearntoassociate
those examples with their correct classification. Crucially, the network
learnstogeneralizesothat,whenpresentedwithanimageordatapattern
that it has not seen before, it can reliably classify it provided that similar
examples existed in the training set. This is a powerful technique that
enablesadriverlesscar,forexample,torecognizeaStopsigninthestreet.
However, it is important to remember that the algorithm will not under-
stand what that classification means. To go beyond a simple classification
label requires knowledge-based AI. That’s the same AI that has its roots
in the expert systems that started to evolve in the decades following the
Dartmouth conference.
So, any practical AI system today needs to use a mixture of techniques
fromtheAItoolbox.AIsystemscannowperformamazingfeatsfasterand
FOREWORD vii
morereliablythanahuman.Nevertheless,weshouldnotgetcarriedaway.
Any current AI is always confined to a narrow task and has little or no
conceptualizationofwhatitdoes.So,despitesomeveryrealandexciting
AI applications, we are still a long way from building anything that can
mimic the broad range of human intelligence convincingly. Furthermore,
our current models of AI are not leading in that direction. A new and
unexpectedmodelcouldtakeusbysurpriseatanytime,butIdon’texpect
to see it in my lifetime.
Evenwithinitscurrentlimitations,AIisstartingtotransformthework-
place. It is already assisting professionals in pushing the boundaries of
their specialism. Examples range from cancer care to improved business
and economic management. AI also has the potential to remove dull and
repetitivejobs.Thereisthetantalizingpossibilityofanewworldorderin
which we work less and enjoy more leisure and education. Such a possi-
bilitycreatesitsownchallengesthough,asitwillrequireustore-structure
our societies accordingly.
The COVID-19 pandemic of 2020–21 has shown that societies are
capable of sweeping changes, as livelihoods have changed and home-
working has become the norm for many. AI has shown its value
by supporting scientists in COVID-19 applications that include repur-
posing existing drugs, diagnostic interpretation of lung images, recog-
nizing asymptomatic infected people, identifying vaccine side-effects, and
predicting infection outbreaks.
Whilethehuman-likequalitiesinAIarestillrathershallow,nowis the
right time to grapple with the bigger ethical questions that will arise as
its capabilities grow. Even now, there are questions over the degree of
autonomy and responsibility to grant to an AI. Further, if it goes wrong,
who will be responsible? If we ever create an AI that is truly human-
like, will it have its own rights and responsibilities? While we are a long
way from that scenario, now is the time to debate, discuss, educate, and
legislate for a future world in which AI is a dominant player.
∗ ∗ ∗
Portsmouth, UK Adrian A. Hopgood
Adrian A. Hopgood is Professor of Intelligent Systems at the University of
Portsmouth, UK.
Contents
Part I Political Economy
1 Social Production and Artificial Intelligence 3
Ryan David Kiggins
2 The Role of Women in Contemporary Technology
and the Feminization of Artificial Intelligence and Its
Devices 17
Mirjam Gruber and Roland Benedikter
3 Rise of the Centaurs: The Internet of Things
Intelligence Augmentation 39
Leslie Paul Thiele
4 AI in Public Education: Humble Beginnings
and Revolutionary Potential 63
Kenneth Rogerson and Justin Sherman
5 Chinese and U.S. AI and Cloud Multinational
Corporations in Latin America 85
Maximiliano Facundo Vila Seoane
6 AI Application in Surveillance for Public Safety:
Adverse Risks for Contemporary Societies 113
David Perez-Des Rosiers
ix
x CONTENTS
Part II Global Security
7 Artificial Intelligence for Peace: An Early Warning
System for Mass Violence 147
Michael Yankoski, William Theisen, Ernesto Verdeja,
and Walter J. Scheirer
8 Between Scylla and Charybdis: The Threat
of Democratized Artificial Intelligence 177
Ori Swed and Kerry Chávez
9 Comparison of National Artificial Intelligence (AI):
Strategic Policies and Priorities 195
Sdenka Zobeida Salas-Pilco
10 Militarization of Artificial Intelligence: Progress
and Implications 219
Shaza Arif
11 Artificial Intelligence and International Security 241
Nitin Agarwala and Rana Divyank Chaudhary
Index 255
