10.6092/issn.2038-3460/17465
application/pdf
2038-3460
17465
1
en
María Menéndez-Blanco
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
Ruha Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code, Polity, 2019
Text.Serial.Journal
https://tecnoscienza.unibo.it/
11
Book Reviews
2020-11-12
2023-06-27
2020-11-12
2023-10-12
81-85
TS
María Menéndez-Blanco
2020/11/12
10.6092/issn.2038-3460/17465
81
University of Copenhagen and Free University of Bozen-Bolzano
2038-3460
1
en
85
Ruha Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code, Polity, 2019
https://tecnoscienza.unibo.it/article/download/17465/16314
11
Book Review                                                              81
   Ruha Benjamin
Race After Technology: Abolitionist Tools for the New Jim Code.
Cambridge, Polity, 2019, pp. 172

María Menéndez-Blanco University of Copenhagen / Free University
of Bozen-Bolzano

    Race after Technology is aligned with a growing body of work on crit-
ical data studies that seek to unpack forms of social injustice embedded
in algorithms and data practices. To that purpose, the book “integrates
the tools of science and technology studies (STS) and critical race stud-
ies” (p. 34) within the framework of race critical code studies. At the core
of this framework is the STS-inspired metaphor of the “black box”. In
the book, this metaphor helps situate algorithms and data practices as
kinds of sociotechnical systems with well-known external effects and
opaque internal functions. Interestingly, the book does not focus opening
the “black box” of algorithms and data practices, understood as in
providing an account of their internal mechanisms. Instead, the focus
seems to be on unpacking the external effects, and their relationships,
which are not only well-known and visible but also oftentimes discrimi-
nating and socially unjust.
    Methodologically, the focus on unpacking external effects and their
relationships is approached by drawing upon “thin description as a
method for reading surfaces” (p. 45). As described in the introduction,
“thinness” is considered an approach to knowledge production that al-
lows for analytic flexibility by tracing links between surfaces. This ap-
proach emphasizes exposing relationships rather than deepening in their
underlying phenomena. Indeed, the book is an incredibly rich source of
examples that illustrate how systemic forms of racism, sexism, and
classism produce and are reproduced in technologies. However, this
richness can be at times overwhelming. The focus on illustrating connec-
tions rather than on elaborating comparisons helps construct a large mesh
of examples. In this way, this approach succeeds in conveying the inter-
woven complexity of the concerns at stake; however, it can be easy to get
lost in all the ramifications and relationships.
    As the examples of systemic forms of racism, sexism, and classism un-
fold, the book makes a solid case for the need to hold public accountabil-
ity of automated data products. These products being job placement pro-
cesses, refugee placement algorithms, or loan risk predictions. These con-
temporary examples are often referred to as the “New Jim Code”, mean-
ing forms of systemic bias embedded into technologies that monitor and
measure people differently based on race, class, or gender. This neolo-
gism is inspired by the Jim Crow Laws, which created legal separations by
race in 26 states of the United States of America from 1881 to 1964. Even
82                                                          Tecnoscienza – 11 (1)

though these laws were formally abolished more than 50 years ago, the
book shows how their legacy is still very present. In the book, these laws
serve as lenses that help reveal how technologies produce, reproduce, and
amplify separations in ways that sometimes are invisible and normalised.
Similar to the inspiration for the “New Jim Code”, most of the examples,
terms, policies, and historical events in the book are situated in the Unit-
ed States of America. Indeed, while reading this book together with some
of my colleagues at the Confronting Data Co-Lab of the University of
Copenhagen, many of the questions we posed ourselves were related to
which theories, laws, or empirical evidences would set the ground for a
race critical code studies from a European perspective.
    Interestingly, an example of the European movement of the Luddites
helps illustrate what for me it is the main argument of the book. The
Luddites were a group of English textile workers who revolted against
manufacturers who used machines in nineteenth-century England. Now-
adays, the term is still used to describe those who oppose technology.
However, the actual meaning of their protest was not the technology in
itself but the “social cost” of developing these technologies. Similarly, this
book denounces the societal costs of automated data products by expos-
ing relationships, opening up ways to engage with data technologies, and
inciting to imagine more socially just alternatives. In my interpretation,
this approach is aligned with an anti-essentialist perspective on Luddism
(Woolgar 1997). From this perspective, the new technical artefacts that
originated the opposition by the Luddites did not have fixed attributes;
instead, the artefacts became part of an existing network of actants with a
distribution of power. The key question for the workers (Luddites and
non- Luddites), entrepreneurs, and other actors involved was “whether
and what effect and for whom could the new machinery be enrolled as
allies?” (Woolgar 1997, 54). Similarly, the key questions posed in this
book tackle matters of power, how new (digital) technologies can pre-
serve or challenge the status quo, and who is represented in imagining
new (digital) futures.
    Zooming into the actual structure of the book, the first four chapters
discuss how technologies help produce social inequality, starting with the
most obvious ways of engineered inequality to more subtle forms of sys-
temic bias such as technological benevolence. The last chapter takes a
slightly different angle, as it focuses on design practices and imagining
futures. Explicit illustrations of the systemic biases embedded into tech-
nologies are described in Chapter 1. These examples range from the first
even Beauty AI contest to a myriad of types of social credits. A particular-
ly interesting insight is that these technologies are usually described in
terms of innovation and forward-thinking, which impact the way they are
represented publicly. More concretely, current innovation narratives tend
to package AI-based systems in a mystical aura that makes their decisions
magically more neutral, fair, and objective than their human counterparts.
Book Review                                                              83
The ways in which algorithms are, and become, represented in society
have an impact on which qualities and attributes become normalized and
accepted; as when social media claim to know what is most important to
the public through algorithmically generated “trends” (Gillespie 2012).
Therefore, the “politics of representation” (Gillespie 2012, 19) become
especially relevant as algorithms are increasingly considered neutral, fair,
and objective in estimating, assessing, and predicting societal matters. Re-
latedly, the book contains many reminders that algorithms are not better
than the people that create them; indeed, they can potentially be more
harmful because of their scope, recursive nature, and limited accountabil-
ity. A related aspect to the innovation narrative is intentionality, meaning
that these systems are created with the intention of creating better worlds.
However, harmful decisions can be morally covered by a rhetoric based
on good intentions. Some of the recent work in critical data studies is
aligned with this line of thinking and proposes that moving toward more
desirable futures entails revising the current focus on individual account-
abilities by, e.g., considering ways to enact public reason (Binns 2018).
     Moving towards more subtle ways of discriminations, Chapter 2 fo-
cuses on instances of unfair and unjust systems that pass off as a “minor
problem” (p. 77). These issues usually remain unnoticed and sometimes
become visible in technologies in use. The examples in this chapter illus-
trate how glitches in the system are not exceptions to faulty technologies
but peepholes that allow looking into the assumptions and stereotypes
that are seamlessly integrated in the development and production of algo-
rithms and data practices. One of the examples is Google Street maps
reading aloud Malcolm “ten” Boulevard instead of Malcolm “X” Boule-
vard. This supposed “glitch” in the text-to-speech system illustrates some
of the design assumptions that eventually dispossess the street name from
its original legacy. From a design perspective, these “glitches” are very
interesting since they can be instrumental in making concerns about dis-
crimination visible and therefore open opportunities to imagine different
futures. Indeed, making things visible is a common argument for social
justice and democracy; however, is visibility always desirable?
     Chapter 3 unfolds the complexity of exposing race in and through
technology, and how there are cases in which visibility can be a “trap”.
Visibility is discussed in many different forms, from literal examples of
photo cameras designed to expose “whiteness” to concerns about how
visibility is enacted to predict in which geographical areas crime is more
likely to happen. Many of the examples illustrate ways in which combin-
ing visibility and predictive algorithms can be especially harmful and dis-
criminatory. Algorithmic-based predictions rely on data to make their es-
timations. Thus, depending on the circumstances and consequences, it
might be convenient to be visible while in other cases invisibility can be
an asset. The extent to which people can decide whether to be visible or
remain hidden relates to issues of power and perpetuation of existing dis-
84                                                          Tecnoscienza – 11 (1)

criminatory systems. This points to the importance of explicitly standing
up against the “datafication of injustice” meaning that “the hunt for more
and more data is a barrier to acting on what we already know” (p. 116).
The rigidity associated with data processes hinders possibilities of includ-
ing different points of view and representations. In this regard, Seaver
(2017) has proposed tactics to enact algorithms ethnographically, which
help approach them as rich sociotechnical systems rather than con-
strained and procedural formulas. Relatedly, there are more and more
initiatives that try to imagine different worlds in which data can help
produce desirable futures; however, some of them can be quite problem-
atic.
    Examples of technologies that try to “fix” the system are described in
Chapter 4. These include attempts to fixing diversity, race, and health,
with interesting practical examples and reflections on how some techno-
logical narratives around diversity monetize differences. Something par-
ticularly interesting is how this chapter draws a line from the Jim Crow
Laws, which sought to identify people’s race to discriminate effectively,
to the New Jim Code, which seeks to provide technical fixes to effectively
meet everyone’s needs on the bases of supposedly stable group identities.
The line from the Jim Crow Laws to the New Jim Code is paved with
tech design imagination. Well-intended technologies can be harmful and
insidious, especially if presented as agents toward better futures. There-
fore, as argued in the last chapter, it is important for tech design to be
aware of how race and technology shape each other. Here the book refers
to many different design-related notions and terms, such as design think-
ing, empathy, and design justice. It feels like the starting point of another
book, rather than a closure. Also, some of the arguments seem to remain
at the shiny surface of what sometimes is understood as design. However,
there are other substantial forms of design that might be well-aligned
with many of the issues raised in the book: for example, in the context of
gender, tech, and design several projects and initiatives (such as fempow-
er.tech and femtech.dk) are trying to move away from deficit approaches
to issues of gender in computing. These projects seek to challenge stereo-
types and assumptions that led to the systematic and structural mecha-
nisms that make computing an exclusive field and discipline.
    In summary, Race after Technology is an excellent read on why it is
important to decode systemic bias embedded into technologies from a
race critical code studies perspective. The book makes a timely contribu-
tion to a growing corpus of work on critical data studies, and it might be
interesting to read it in conjunction with other contemporary books (e.g.
D'Ignazio and Klein 2020; Eubanks 2018). Integrating the tools of STS
into race critical code studies, this book makes a compelling case for how
race is not only a social construction, but it also constructs realities where
race and technology shape one another. Many of the arguments are prob-
ably very familiar to researchers in STS; however, the examples can be
Book Review                                                                  85
instrumental in opening up important discussions among actors such as
researchers, developers, designers, students, or policymakers. Indeed, the
author’s clear and down to earth writing style makes this book very en-
gaging for anyone interested in how algorithms and data practices embed
forms of social injustice and how these can be considered when imagining
better futures.

References
Binns, R. (2018) Algorithmic accountability and public reason, in “Philosophy
       and Technology”, 31 (4), pp. 543-556.
D'Ignazio, C. and Klein, L. F. (2020) Data feminism, Cambridge, MA, MIT
       Press.
Eubanks, V. (2018) Automating inequality: How high-tech tools profile, police,
      and punish the poor, New York, St. Martin's Press.
Gillespie, T. (2012) Can an algorithm be wrong?, in “Limn”, 1 (2).
Seaver, N. (2017) Algorithms as culture: Some tactics for the ethnography of al-
        gorithmic systems, in “Big Data and Society”, 4 (2), pp. 1-12.
Woolgar, S. (1997) The Luddites: diablo ex machine, in K. Grint and S. Woolgar
      The Machine at Work: Technology, Work and Organization, Cambridge,
      Polity Press.



                                      ***
    Stefano Crabu
Dalla molecola al paziente. La biomedicina nella società contemporanea
[From the Molecule to the Patient. Biomedicine in Contemporary Society],
Bologna, Il Mulino, 2017, pp. 179

Lorenzo Beltrame Università di Trento

    “From bench to bedside” is the motto of the emerging translational
research in current biomedicine. In the biomedical literature, translation-
al research is promoted as a strategic and efficient way to implement the
novel discoveries of biological science in clinical practices, and to incor-
porate clinical observations back to laboratory science. In Science and
Technology Studies (STS) and in social studies of biomedicine, transla-
tional research is addressed as a space of problematization instead, which
invests important transformations in the articulation of clinical and ex-
perimental practices as well as discourses and epistemologies, the genera-
tion of novel biological entities and, finally, the making of subjectivities.
