10.6092/issn.2038-3460/17571
application/pdf
2038-3460
17571
1
en
Federico Cugurullo
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
Kate Crawford, Atlas of AI, Yale University Press, 2021
Text.Serial.Journal
https://tecnoscienza.unibo.it/
13
Book Reviews
2022-07-07
2023-06-30
2022-07-07
2023-10-26
157-160
TS
Federico Cugurullo
2022/07/07
10.6092/issn.2038-3460/17571
157
Trinity College Dublin
2038-3460
1
en
160
Kate Crawford, Atlas of AI, Yale University Press, 2021
https://tecnoscienza.unibo.it/article/download/17571/16421
13
Book Review                                                                       157

political dimension of the term “platform”, which I will certainly not reveal
in this review, hoping in this way to further intrigue readers reading this
book and the dynamics related to digital work.

References
Aytes, A. (2012) Return of the Crowds: Mechanical Turk and Neoliberal States of
       Exception, in T. Scholz (ed.), Digital Labor: The Internet as Playground and
       Factory, New York, Routledge, pp. 79-97.
Crawford, K. (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial
      Intelligence, New Haven and London, Yale University Press.
Gray, M.L. and Suri, S. (2019) Ghost Work: How to Stop Silicon Valley from Build-
       ing a New Global Underclass, Boston, Houghton Mifflin Harcourt.
van Dijck, J., Poell, T. and de Waal, M. (2018), The Platform Society: Public Values
       in a Connective World, Oxford, Oxford University Press.
Zuboff, S. (2019) The Age of Surveillance Capitalism: The Fight for a Human Future
       at the New Frontier of Power, New York, Public Affairs.

                                         ***

    Kate Crawford
Atlas of AI, New Haven and London, Yale University Press, 2021, pp.
327

Federico Cugurullo Trinity College Dublin

    Artificial intelligence (AI) is an unknown territory. It is what Renais-
sance cartographers would have called terra incognita: a Latin expression
signifying unknown lands that have been barely explored. And what is
more mysterious and undocumented these days than AI? The concept of
AI evokes a multitude of diverse non-biological intelligences capable of
learning independently, thinking in a rudimentary manner and acting with-
out being supervised, in a variety of urban spaces and domains, ranging
from cheap restaurants to the highest levels of governance (Cugurullo
2021). Somehow paradoxically AI is everywhere, and yet its geography and
politics remain largely uncharted.
    It is precisely in this context, rich in cartographic and epistemological
challenges, that Kate Crawford’s work, researcher at Microsoft Research
and chair of AI and Justice at Paris École Normale Supérieure, is situated.
Over years of empirical research, she has extensively explored what AI is
made of, where AI is coming from, what it is impacting on and how. The
results of her studies are now culminating in a fascinating book: an atlas of
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artificially intelligent technologies, which is simultaneously science and art,
a geographical inquiry and a political intervention.
    Crawford’s Atlas of AI is an unusual atlas, since the author adopts a
prominent narrative approach and, rather than a mere collection of maps
and analyses, the book offers a collection of stories. Crawford is a gifted
storyteller, and her tales of AI are divided on the basis of six main themes
that form the core chapters of the book: Earth, Labor, Data, Classification,
Affect and State. In the first part of the book, Earth, the author takes us on
a journey to Nevada where AI, as a material product, is made. AI is pre-
sented as an extractive technology whose creation requires many minerals
and metals and whose life depends upon electrical energy. The line of in-
quiry is here very much in sync with the work of economic geologists and
critical geographers who have unpacked the supply chains of the critical
materials that are behind the provision of smart tech (Zhou et al. 2021).
Not only is Crawford’s critique sharp. Her voice speaks the language of
multiple disciplines, and a key strength of this book is in the solid bridges
that it creates to connect diverse fields of research which together can fully
illuminate the nature of AI.
    The second part of the book, Labor, convincingly shows the human as-
pects of AI, stressing that the industry of AI would not be possible without
the physical and mental exertion of thousands of human beings. To explore
this critical aspect, the journey continues in the US where Crawford lets us
enter an Amazon’s fulfilment center in New Jersey. The space is gigantic
and the labor dynamics that we witness are a Marxian nightmare: those
very humans who are working hard to create robotic technologies are
themselves being treated like robots.
    The third and fourth parts, Data and Classification, are deeply intercon-
nected, since they deal with the production of the digital information that
AIs are fed with, and how artificially intelligent entities categorize and me-
tabolize these flows of information. Data is everything. It is everything in
the sense that it is the most important resource for any company wishing
to create and train an intelligent machine. Data is also everything because
every piece of both the digital and physical world contains some form of
information that AI companies can extract and then feed their machines
with. Crawford exposes and denounces the culture of data extraction
which seems to know no limit despite its many flaws in terms of privacy
and ethics. Singing from the same hymnbook of critical data scholars like
Kitchin (2022), she shows how data is not neutral and its metabolization
inside AI tech reproduces power relations and biases. The datasets em-
ployed by AI contain human-made worldviews which often amplify social
inequality under the banners of rationality and scientific objectivity.
    The same leitmotif continues throughout the penultimate part of the
book, Affect, which deals with one of the most controversial topics in AI
research: emotions. Here the author, taking a stand unlike that of some
Book Review                                                                 159

STS scholars (see Hillersdal et al. 2020), equates affect with emotion and
asks the following critical question: can artificial intelligences recognize our
emotions and then predict our behavior? In the AI industry, the answer to
such questions would be an obvious yes, but Crawford does not take the
mainstream discourses surrounding AI as articles of faith. Instead, she
traces back and critically unpacks the roots of contemporary facial recog-
nition systems, stressing for instance that many datasets are built upon the
work of actors who are simulating emotions. Her conclusion is that what
the AI is commonly learning from are thus faked emotions whose impact
is however very real. AI hiring companies use these flawed systems to eval-
uate people’s suitability for a job, and police officers rely on predictive sys-
tems to identify potential suspects. AI is becoming the lens through which
society and its future are observed, but this lens is often cracked and even
a tiny crack on the surface can generate a huge distortion.
     In the final parts of the book, via two interconnected chapters, State
and Conclusion, Crawford digs deep into questions of politics and power.
These final chapters are in the tradition of Science and Technology Studies.
The work of STS scholars such as Winner (1978) is employed to portray
AI as an instrument of power which, far from being politically neutral, is
frequently designed to punish rather than to support. This is because, as
Crawford explains, most AI systems go back to military systems, which is
a point that resonates with Suchman’s (2020) recent studies. Their original
logic was to find and eliminate threats, and now the same punitive logic is
filtering down to schools, workplaces, hospitals and police stations. It is
leaving the battlefield to enter our everyday life.
     By reading Atlas of AI, the picture that emerges is that of a technology
that, to paraphrase Winner (1978), has got out of control: an autonomous
technology. Essentially, this has happened for two reasons. First, because
while in the beginning AI technologies were instruments in the hands of
the state, intentionally crafted for military purposes, they are now being
increasingly privatized. States do not control AI anymore. The governance
of AI is a complex mix of private and public forces and interests, which
mirrors the classic neoliberal implementation of smart tech (Karvonen et
al. 2019). Second, because we scarcely understand AI and its capabilities,
and we will never be able to fully control what we do not fully understand.
     Atlas of AI is the perfect medium to begin to understand AI. Crawford
wisely avoids any form of jargon and her message comes across clear and
loud. The book also contains a wide array of notes and references which
the more experienced readers will find very useful to go deeper into the
several themes that Crawford’s atlas illustrates, but also to find new direc-
tions for future research. There are many more uncharted lands that await
AI researchers, including emerging human-machine relations, thorny ethi-
cal dilemmas and questions of governance at a time when autonomous
technologies are making decisions about our life (Stilgoe 2018). There is a
160                                                                Tecnoscienza – 13 (1)


sense of urgency that social scientists in particular cannot help but feel.
The reason is that AI is not simply terra incognita. It can also be finis terrae:
the end of the world. The radicality of AI tech is such that it might cause
the end of cities and societies as we know them (Cugurullo 2021). It is time
to be brave, face our deepest fears and explore the unknown. We already
have an excellent guide book and it is Atlas of AI.

References
Cugurullo, F. (2021) Frankenstein Urbanism: Eco, Smart and Autonomous Cities,
      Artificial Intelligence and the End of the City, Oxon and New York,
      Routledge.
Hillersdal, L., Jespersen, A.P., Oxlund, B. and Bruun, B. (2020) Affect and effect in
        interdisciplinary research collaboration, in “Science & Technology Studies”,
        33 (2), pp. 66-82.
Karvonen, A., Cugurullo, F. and Caprotti, F. (eds.) (2019) Inside Smart Cities:
      Place, Politics and Urban Innovation, London and New York, Routledge.
Kitchin, R. (2022) The Data Revolution: A Critical Analysis of Big Data, Open Data
       and Data Infrastructures, London, Sage.
Stilgoe, J. (2018) Machine learning, social learning and the governance of self-driving
        cars, in “Social Studies of Science”, 48 (1), pp. 25-56.
Suchman, L. (2020) Algorithmic warfare and the reinvention of accuracy, in “Critical
      Studies on Security”, 8 (2), pp. 175-187.
Winner, L. (1978) Autonomous Technology, Cambridge, MA, the MIT Press.
Zhou, L., Fan, H. and Ulrich, T. (2021) Editorial for Special Issue “Critical Metals
       in Hydrothermal Ores: Resources, Recovery, and Challenges”, in “Minerals”,
       11 (2), p. 299.

                                         ***
