10.6092/issn.2038-3460/17569
application/pdf
2038-3460
17569
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Guilherme Cavalcante Silva
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
Massimo Airoldi, Machine Habitus: Toward a Sociology of Algorithms, Polity Press, 2022
Text.Serial.Journal
https://tecnoscienza.unibo.it/
13
Book Reviews
2022-07-07
2023-06-30
2022-07-07
2023-10-26
148-152
TS
Guilherme Cavalcante Silva
2022/07/07
10.6092/issn.2038-3460/17569
148
York University
2038-3460
1
en
152
Massimo Airoldi, Machine Habitus: Toward a Sociology of Algorithms, Polity Press, 2022
https://tecnoscienza.unibo.it/article/download/17569/16419
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148                                                        Tecnoscienza – 13 (1)


   Massimo Airoldi
Machine Habitus: Toward a Sociology of Algorithms, Cambridge, Polity
Press, 2022, pp. 192

Guilherme Cavalcante Silva York University

     A lot has been written about algorithms over the past decade. The idea
of algorithms as neutral and value-free is being challenged even at a main-
stream level, as works like Safiya Noble’s Algorithms of Oppression (2018)
and Shoshana Zuboff’s Surveillance capitalism (2019) became talking
points in places like the White House. At the same time, productions like
The Social Dilemma (a docudrama released by Netflix in 2020) reached
millions of viewers around the world. So why another book on algorithms
when critical approaches to digital technology, its potential negative social
impact, and the business models of tech companies have flooded academic
classrooms, conference hallways, and even pub night conversations? For
Airoldi, it all comes down to the (lack of) understanding of both what ma-
chine learning systems and what things like social structures are. If all we
have left is reproducing approaches to algorithms that keep splitting the
world between the order “we” humans inhabit on one side and the tech-
nical order on the other, then there is, in fact, not much one could add to
the mainstream critique of algorithms or to avoid the fight between
apocalittici and integrati.
     That is where Airoldi’s Machine Habitus: toward a sociology of algo-
rithms comes in handy. In Chapter 1 (“Why not a Sociology of Algo-
rithms?”), Airoldi fully embraces a sociotechnical view of reality, aiming to
comprehend “how culture enters the code of algorithmic systems, and how
it is shaped by algorithms in turn” (p. 4). Moving away from debates
around human-machine interaction centered on concepts like conscious-
ness and intelligence, Airoldi highlights the importance of an agentic no-
tion of sociality to account for machine systems as social agents inhabiting
a techno-social structure, something especially true for machine learning
systems. But is that all? Recognizing algorithms as social agents is all that
is left as a research agenda? If that is the case, then Airoldi came too late
to the party as critical algorithm studies contributions have for more than
a decade now recognized algorithms as fully integrated within sociotech-
nical assemblages (e.g., Gillespie 2014). However, there is much more to
the book than that. For Airoldi, it is not enough for STS scholars to address
human-machine interactions in terms of “sampling bias,” “collection meth-
ods,” or subjective limitations on the part of the agents involved in the
technological process (Symons and Alvarado 2016, 5). Sociological and po-
litical inquiries of machine systems should also not have to choose between
focusing on some sort of soft “technological determinism” on one side and
the resistance capabilities of agentic subjects, changing technology from
Book Review                                                               149

below, on the other. To understand machines as social agents, we need to
consider how machines are socialized and how socialized machines partic-
ipate in society. For that, Airoldi extends Bourdieu’s concept of “habitus”
to the study of algorithms and machine learning systems.
     In Bourdieu’s words, habitus are “systems of durable, transposable dis-
positions, structured structures predisposed to function as structuring struc-
tures” (Bourdieu 1977, 72, emphasis added). For Airoldi, the main insight
from Bourdieu’s theory of habitus is that it allows us to talk about agents
and their actions not as a result of deterministic impulses from the “out-
side” or self-determined. Agents act within habitus in the sense that their
actions are structured structures, that is, the result of embodied disposi-
tions from one’s environment and social setting, whilst being structuring
structures, in the sense of ordering and changing the very social structure
they inhabit. Airoldi uses the concept to complicate (in a good way) socio-
logical studies of machine systems. For him, habitus is not just an attribute
of humans as we can look at machines through the lenses of habitus. After
all, he says, “the code is in the social world, but the social world is in the
code” (p. 28). Cultural propensities and social structures are encoded in
machine learning systems that, at the same time, shape what these social
structures look like. Our techno-social environment is neither the effect of
algorithmic oppression nor human achievements (or failures) but a result
of the interactions between human habitus and machine habitus.
     Airoldi devotes Chapter 2 and 3 to understand the dynamics of the cul-
ture in the code and the code in the culture, respectively. The most com-
mon answer in critical data studies and STS for the question “where does
the culture in the code come from?” (p. 36) would probably be through
the cultural biases that come to the machine through design or from the
code’s creators. An overwhelming amount of works over the past few years
have focused on how to solve the problem of cultural biases in the design
of technology and create a more inclusive and equal digital environment.
That includes scrutinizing the definitions of what the algorithms are sup-
posed to evaluate (e.g., “relevant,” “high risk,” “meaningful”) and also da-
tabases, statistical postulates, and methods employed by companies or
public agents for algorithms to function. However, for Airoldi, a sociology
of algorithms cannot just be a sociology of algorithm creators (or of deus
in machina), especially with the rise of unsupervised machine learning sys-
tems. The role of trainers, for instance, those who advertently or inadvert-
ently “prepare” the data for machine learning systems, has been over-
looked. There is more to the sociotechnical analysis of algorithms than
looking at companies, economic models, or designers. Following Bour-
dieu’s habitus, Airoldi invites us to look at the specific cultural contexts
which give rise to machine habitus, starting with the local and global data
contexts for and through which machines operate and make sense of the
150                                                         Tecnoscienza – 13 (1)


world. Like with humans, machines’ predispositions and affordances, in-
herited by design, would interact with the cultural structures through time,
producing specific forms of socialization and internalizing culture in dif-
ferent ways. Those specificities would add more complexity to the study of
algorithms in fields like STS, for example.
    On the other hand, the very cultural structures in which machines are
socialized are also shaped by the code. This symbiosis is never symmetric
as the interactions always occur between different habitus and cultural pro-
pensities. Humans never respond to machinic input with the expected out-
put and vice-versa. In his words, “interaction orders of humans and ma-
chines blend within the layered techno-social order of the Internet” (p. 89).
Tracing causal relations between algorithms and social world changes will
only produce technocratic and fatalist accounts.
    Airoldi highlights that interactions between users and machines always
occur in peculiar environments (platforms and their business models),
which in their turn are always shaped by cultural and economic fields. Both
users and machines learn from each other, as even the famous feedback
systems, so commonly mentioned in relation to filter bubbles, work both
ways. Finally, interactions between users and machines are always crossed
by what he terms “informational asymmetry”, the fact that the knowledge
the user has of the machine functioning is not always the same a machine
has of the user, and “cultural alignment,” or whether the propensities of
socialized machines “match” with the ones of the users (or whether there
is a clash between habitus[es]). He proposes a typology of four types of
user-machine interactions (which entails reinforcement, co-production,
transformation, and disillusionment), a useful theoretical contribution for
STS scholars to address the code's relations and influence on culture vice-
versa.
    In the final two chapters, “Theory of Machine Habitus” (Chapter 4)
and “Techno-social reproduction” (Chapter 5), Airoldi seeks to answer the
following questions: “what is the extent of machine habitus in comparison
to the original “habitus” theorized by Bourdieu? How do different pro-
pensities “embodied as habitus and encoded as machine habitus” (p. 110)
mediate human life in techno-social fields? What are the effects of the en-
tangled relations between humans and socialized machines over time and
what are the global (overall) effects of such relations? For the first one, to
understand the differences between machine habitus and the original hab-
itus, it is important to account for the limitations given to machine habitus
by its digital infrastructure, namely platforms. As recent STS scholarship
has demonstrated (e.g., Helmond 2015), platforms offer particular af-
fordances to machine learning systems, from a variety of levels, including
political-economical, that “modulate possibilities of action” (p. 117).
Airoldi brings a concept very dear to STS for the second and fourth ques-
Book Review                                                                 151

tions: that of entanglements. He warns scholars that there is never an inter-
action between autonomous me and an autonomous machine, but a rela-
tion between a set of cultural propensities mediated by an active environ-
ment (platform) and different habitus (what he mentioned when referring
to cultural alignment). “There is nothing personal in automated music rec-
ommendation,” for instance, as “things like taste and behaviour are a prod-
uct of shared social conditions” (p. 121). How the author relates the idea
of entanglements and its ontological indistinguishability perspective with
other terms he uses, like sociotechnical order or sociotechnical evolution,
is nowhere to be found in the book, perhaps a shortcoming not only of the
book but of certain STS approaches lacking theoretical rigor when using
terms interchangeably.
    For the third question, he highlights the importance of another concept
dear to STS scholars: boundaries. For him, at least four processes of
boundaries happen in techno-social fields over time: boundary differenti-
ation (reinforcing the local culture of social subjects – e.g., filter bubbles),
boundary fragmentation (nudging users towards certain behaviors or di-
rections they are not familiar with), boundary mobilization (reinforcing the
global scale, collective culture of social subjects – e.g., Google’s autocom-
plete algorithm), and boundary reconfiguration (transforming practices of
users with a top-down approach – e.g., algorithmic ranking on Instagram).
    The inclusion of these typologies and concepts showcases Airoldi’s
main objective with the book: to amplify the scope of sociological analysis
of machine systems beyond calls to “fix” biases. These strategies aim to
“provide researchers interested in the social world with ways to include
artificial agents in their analyses, and researchers studying artificial agents
with ways to consider them as part of the social world” by means of “in-
vestigating machine learning systems as social agents culturally entangled
with humans in the context of platformized fields” (p. 149).
    All in all, Airoldi provides a powerful reimagination of the study of ma-
chine learning systems. Fields like STS and Critical Data Studies have been
looking at particular instances of human-machine interaction, such as eth-
nographies of machine design or even of algorithms per se (e.g., Delfanti
2021), studies of user reception and interaction with algorithms, and plat-
form studies investigating the political economy of digital platforms.
Airoldi adds to these specific research strategies a solid theoretical back-
ground that brings together the most fundamental concerns at play in hu-
man-machine interaction. While not disregarding the contribution of crit-
ical approaches to algorithms that focus on issues of bias and “incorrect”
databases, he offers researchers a chance to investigate such issues “in light
of the socio-cultural data contexts behind its formation” (p. 156). How-
ever, I wonder how further researchers should take the concept of habitus
to address those fundamental concerns. Airoldi himself recognizes limita-
tions in Bourdieu’s original concept, even though he avoids spending too
152                                                            Tecnoscienza – 13 (1)


much time addressing criticisms of Bourdieu’s formulation, especially how
habitus in Bourdieu appears as some sort of a “print” with little possibility
of change over time. While machine habitus is a fascinating and useful in-
sight, studying techno-social reproductions and machine socialization can
move through different paths as the ones formulated by Bourdieu decades
ago.
    This book should be of interest to any STS scholar investigating human-
machine interactions, in particular to early-career scholars and STS gradu-
ate students who want to not only study the impact of machine learning
systems on society but also empirically understand the ways in which ma-
chines become part of society in the first place and in which users, design-
ers, policymakers, and machines are entangled in techno-social structures.

References
Bourdieu, P. (1977) Outline of a Theory of Practice, Cambridge, Cambridge Uni-
       versity Press.
Delfanti, A. (2021) The Warehouse: Workers and Robots at Amazon, London,
       Pluto.
Gillespie, T. (2014) The relevance of algorithms, in T. Gillespie, P.J. Boczkowski
        and K.A. Foot (eds.), Media Technologies: Essays on Communication, Ma-
        teriality and Society, Cambridge, the MIT Press, pp. 167-194.
Helmond, A. (2015) The platformization of the web: Making web data platform
      ready, in “Social Media + Society”, 1 (2), pp. 1-11.
Noble, S. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism,
       New York, New York University Press.
Symons, J. and Alvarado, R. (2016) Can we trust Big Data? Applying philosophy of
      science to software, in “Big Data & Society”, 3 (2), pp. 1-17.
Zuboff, S. (2019) The Age of Surveillance Capitalism: The Fight for a Human Future
       at the New Frontier of Power, New York, Public Affairs.

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