Table Of ContentThe Prospect of a Humanitarian
Artificial Intelligence
Also available from Bloomsbury:
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by Eric Dietrich, Chris Fields, John P. Sullins,
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The Philosophy and Science of Predictive Processing,
edited by Dina Mendonça, Manuel Curado, and Steven S. Gouveia
The Prospect of a Humanitarian
Artificial Intelligence
Agency and Value Alignment
Carlos Montemayor
BLOOMSBURY ACADEMIC
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First published in Great Britain 2023
Copyright © Carlos Montemayor, 2023
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Contents
List of Figures vi
Preface vii
Acknowledgments xi
Glossary and Abbreviations xiii
Introduction: Normative Aspects of AI Development 1
1 Intelligence and Artificiality 15
2 General Intelligence and the Varieties of AI Risk—A Hierarchy
of Needs 43
3 The Attentional Model of Epistemic Agency—The Main Source of
Rational Trust in Humans (and Future AI) 83
4 The Handicaps of Unemotional Machines 117
5 The Vitality of Experience against Mechanical Indifference 145
6 Are AIs Essentially Collective Agents? 175
7 The Legal, the Ethical, and the Political in AI Research 201
8 Human Rights and Human Needs 223
Notes 242
References 247
Index 265
List of Figures
1 Goldman’s three spheres of social epistemology—Anthropocentric. ©
Carlos Montemayor in collaboration with Garrett Mindt 183
2 Non-anthropocentric social epistemology. © Carlos Montemayor in
collaboration with Garrett Mindt 184
3 Spheres of a radically expansionist social epistemology. © Carlos
Montemayor in collaboration with Garrett Mindt 185
Preface
We are an adventurous and curious species. Our intelligence is our point of
pride and the basis of our endless interests and achievements. The various needs
and motivations we developed as we evolved have made us both mighty and
vulnerable. We inherited our intelligence from an astonishingly diverse lineage,
and we share much of our intelligence with other species. Yet our intelligence
is unique in its range and power. What is more, our collective intelligence
has profoundly shaped the planet, and not always to the good. For instance,
without the scientific and industrial revolutions, nuclear weapons would never
have been developed. The destructive by-products of the recent expansion and
mechanization of our intelligence are part of our vulnerability.
This book centers on the possibility of creating another colossal industrial
achievement, just as threatening and awesome as the atomic bomb: the set of
computational techniques that may result in artificial intelligence (AI). This
possibility does not exist at present, and we do not know how likely is the
prospect of its existence. What we know is that the industrial revolution would
be dwarfed, a small step in our history, compared to this potential development.
Ironically, AI might also decisively demote our species by making us less
intelligent than our creation, and this has been identified as the most important
risk surrounding AI: the so-called “singularity,” or the moment when we are left
behind (far behind, some fear), outpaced by our newly created super-intelligent
systems. Some have posited that we may find a solution in value alignment, in
ensuring that the computational techniques we create reflect our ethics and
norms, ensuring that it will be beneficial rather than destructive. The value
alignment problem is a major theme of this book.
Value alignment appears to be the best answer to AI critics’ vague predictions
of doom and demotion. A key contribution of this book is to note that
there is no unique “value alignment problem,” but a variety of them. That is
because there are various forms of value alignment, which must be carefully
distinguished from one another. These distinct alignments require meaningful
specifications dependent on contextual information and skills for coordinated
action in different normative domains (moral, epistemic, and political). Human
attunement to what is salient, evidentially important, or morally relevant
viii Preface
depends on how we align our attention. These attentive alignments allow us to
triangulate with each other with respect to what we value. The book, however,
does not merely focus on how to avoid the singularity through strategies for
value alignment with AI. A central concern is the control and power—epistemic,
social, and political—that AI research is already exerting and accumulating.
How probable is it that genuinely intelligent artificial agents will be created?
A clear answer to this question remains elusive, though commentators have
speculated with tremendous enthusiasm. Echoing William James’ opinion
about the state of psychology at his time, Terry Winograd said in 1977 that
developments in artificial intelligence were akin to alchemy—we combine, mix,
and scale up, in the hopes of finding the holy grail, but without knowing when
or how exactly we will succeed. More recently, Melanie Mitchell has returned to
the question, whether today’s AI research still resembles alchemy in “Why AI Is
Harder than We Think.” Her verdict is that, despite enormous progress, large
financial investments, and public media hype, AI research more than forty years
after Winograd’s remark may still be industrial alchemy—nothing more than a
utopian hope.
Mitchell presents several methodological fallacies in AI research that impede
its development. One of them, “wishful mnemonics,” is particularly relevant
for this book: AI researchers import terms from philosophy, psychology, and
neuroscience (e.g., neural networks, attention, intelligence, knowledge, truth)
without any real justification. This is a kind of wishful labeling that would, by
alchemical magic, turn massive data mining and layered statistical processing
into thoughtful agency. Gratuitous use of psychological terms in descriptions
of machine output muddles our evaluations of AI, and confuses everyone with
respect to the real nature and potential of AI technology. This book proposes
a useful way to categorize different kinds of AI, without simply assuming that
psychological terms can be directly applied to all of them equally.
However, if and when AI is developed, the consequences will be dramatic.
We need to prepare ourselves for this eventuality by making our technology
more beneficial to all humans, starting with the technology we are using now.
AI research technology is not benefiting humanity in any clear or measurable
way, and on the contrary, it has been misused and can potentially become quite
dangerous. An important reference point here is Kate Crawford’s book, Atlas of
AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. As Crawford
forcefully argues, the current investment frenzy in AI technologies is profoundly
troubling because of its economic, environmental, social, and political costs.
Power and justice are also a central concern of my examination of AI.
Preface ix
As a matter of basic fairness, if intelligence is developed industrially, then
it should be used for the benefit of all humanity. It should also be accessible
in various ways to all human beings, and not be the exclusive property of a
few powerful impresarios or politicians who might keep AI as an industrial
or military secret, which is consistent with what is currently happening. This
stolen intelligence could potentially be used against all of us. Such a scenario
presents a more perverse kind of being “left behind,” a political maneuver to
demote not ultra-rich or ultra-powerful humans by expelling them from the
production of knowledge. Instead of a “terminator” scenario in which machines
take over, here we have a more standard form of unfairness or “business as
usual.” As a countermeasure to this possibility, this book argues in favor of a
humanitarian approach to the development of AI technology, based on the
existent international framework for the protection of human rights. These
problems are urgent, whether or not the pursuit of AI turns out to be a form of
alchemy, and whether or not AI agents become genuinely intelligent.
Another goal of this book is to demonstrate that we need to distinguish
between formidable predictive computing that looks like intelligence and
genuinely intelligent agency, a goal that can only be achieved by firmly grounding
our arguments in philosophical considerations. With respect to psychology,
critical distinctions are drawn from the empirical and philosophical literature
on intelligence, attention, consciousness, knowledge, and emotion. Value theory,
as well as political and legal philosophy, informs the book’s proposals concerning
the value alignment problem, based on an analysis of human needs. Types of
intelligence are examined according to their normative, or value-grounding
roles. The importance of control and motivation for intelligent behavior is
examined according to basic human needs, which are then shown to be critical
in our understanding of human rights and human dignity.
If the reader is mostly interested in philosophy of mind, epistemology or the
theory of knowledge, and their relation to motivations, needs, and intelligence,
her focus should be on the first five chapters of the book. The first part is about
the conditions under which current technology could be considered genuine
intelligence, and the various problems that these assessments confront. If she is more
interested in social, political, or legal issues, then the concluding three chapters are
the most relevant. The second part is about why AI may be best understood as a
form of collective epistemic agency, and the urgent political repercussion of such a
conceptualization of AI. But I would like to encourage the reader to go through all
of the chapters in order because the account in the first part of the book is deeply
tied to the notion of human dignity explained in the second.
