A sociotechnical perspective for the future of AI: narratives, inequalities, and human control

Abstract

Different people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective.

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Vol.:(0123456789)
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Ethics and Information Technology (2022) 24:4
https://doi.org/10.1007/s10676-022-09624-3
ORIGINAL PAPER
A sociotechnical perspective forthefuture ofAI: narratives,
inequalities, andhuman control
LauraSartori1 · AndreasTheodorou2
Accepted: 5 January 2022 / Published online: 24 January 2022
© The Author(s) 2022
Abstract
Different people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the
alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers,
and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI
could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to
be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of ‘magnifying glasses’
in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explain-
ability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in
novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the
mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional
context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines
of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community
and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and
policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective.
Keywords Artificial intelligence· Sociology· Accountability· Transparency· Narratives· Inequalities· Human control
Introduction
Artificial intelligence (AI) is not a new field, it has just
reached a new ‘spring’ after one of the many ‘winters’
(Boden, 2016; Floridi, 2020). As a matter of fact, we might
be on the brink of a new winter since different actors (firms,
individuals, media and institutions) have concretely started
questioning the over-inflated expectations. It may be the
multiple ongoing narratives, including the ones of moving
from the traditional ‘black-box approach’ to the use of trans-
parent and explainable methods (Guidotti, 2019a, 2019b),
the ‘scary’—but improbable—prospects of creating a
‘superintelligence’ that will convert humans into paperclips
(Bostrom, 2014), or even of an ‘AI race’ between nations
for the development of the ‘ultimate’ algorithm (Houser &
Raymond, 2021). Then again, the term ‘AI’ means different
things to different people; anything from data aggregation
and manipulation to ‘magic’ (Theodorou & Dignum, 2020).
Yet, AI is neither a myth nor a threat to man (Samuel,
1962). In more actual terms, the welcome heated debate
over AI for social good should not forget that AI systems
are neither the ultimate chaos nor the ultimate panacea to
social, political and economic issues of contemporary soci-
eties. Calls for responsible AI are mounting and eventu-
ally shedding light on many overseen social cutting issues.
Responsible AI is built around three pillars: (i) governance;
(ii) mechanisms; and (iii) means of participation (Dignum,
2019, 102–104). It is crucial to stop and think differently
about our autonomous systems by considering them AI
socio-technical systems, i.e. the combination of the technical
component (i.e. the code and—if used—the data) and socio
elements (i.e. the stakeholders responsible for the system
* Laura Sartori
l.sartori@unibo.it
Andreas Theodorou
andreas.theodorou@umu.se
1 Department ofPolitical andSocial Sciences, University
ofBologna, Via Bersaglieri 6, 40125Bologna, Italy
2 Department ofComputer Science, Umeå University,
90187Umeå, Sweden
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L.Sartori, A.Theodorou
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and the society in which the system is deployed) (Dignum,
2019).
In the attempt to avoid futuristic oversimplification
(Floridi, 2020), AI needs a multidisciplinary community.
Afterall, when the field of Artificial Intelligence was first
established at the now famous Dartmouth Workshop,
albeit hosted at the mathematics departments, the partici-
pants included multiple psychologists, cognitive scientists,
economists, and political scientists (McCarthy etal., 1955).
Between the ‘AI winters and springs’, the focus of the field
shifted to technical performance, ignoring the ‘socio’ part
in the socio-technical systems. In recent years, sociology,
among other social sciences, has looked back to the growing
importance of AI with a scattered and ambivalent interest.
Starting with big data analytics, it seemed sociology had to
contribute mainly to the methodological sphere. As applica-
tions to social phenomena, all attention went on the newly
developed tools in computational social sciences, such as
agent-based models and the whole family of machine learn-
ing techniques (Bainbridge etal., 1994; Christin, 2020).
Potential social impacts and implications received scant
attention, although AI proved to have already entered the
ways individuals, firms and public institutions organize pro-
cesses of production, distribution, and exchange as well as
consumption, public opinion, and politics. To encourage a
socio-technical perspective to AI to flourish, our objective is
to highlight some issues that are its building blocks.
First, we address the need for a useful sociology of AI.
This claim has been advanced in the past just before the win-
ter of AI. Now, before the mounting AI bubble bursts again,
it urges to expand a sociological perspective that comple-
ments technical approaches to AI.
Second, this article focuses on the main challenges
associated with autonomous systems, as their use spreads
throughout our societies.
If much interest has been directed to technical possibili-
ties and performances, we understand biases and unfairness
as being among the major challenges to be addressed in
such a sociotechnical perspective. As they are tightly related
with real societal inequalities, we first notice advancements
within the AI technical community that calls for transpar-
ency and explainability, accountability and contestability
with some interesting insights from the social sciences.
Then, a summary of the major results linking AI systems
with inequalities is offered.
Third, the article aims at introducing the topic of nar-
ratives. Individuals, with their hopes and fears, do config-
ure a technological imaginary that plays a crucial role for
the spread, acceptance, and usage of any technology. How
citizens—be they programmers, politicians, entrepreneurs
or citizens—depict and account for AI may influence its
own technical development. Not only do narratives reflect
organizing visions for society, but they also are a tangible
sign of the traditional lines of social, economic, and politi-
cal inequalities. Knowledge about narratives helps in better
addressing future technical developments, public debate, and
policy.
In the following sections, we discuss each one of these
issues in turn. Then, we wrap up our consideration for a
socio-technical approach to AI linking it to the concept of
AI for social good. What does ‘good’ mean for society? Far
from the strict political philosophical considerations, it does
not equate with ‘fixing’ social problems. Socially-aware AI
systems are a first, not the ultimate, step into improving soci-
ety and meeting societal challenges.
A sociology forAI
The interaction between AI and social sciences is a “fruitful
arrangement.” While social sciences contribute to the devel-
opment of socio-beneficial systems—or even in the develop-
ment of AI techniques—AI research has been contributing
back to sociological research.
In parallel with other domains such as philosophy, soci-
ology could contribute to a diverse understanding of AI.
A sociological conception of AI adds up to the open line
of investigation about wide social implications, offering
crucial understanding for designers and developers trying
to anticipate possible negative consequences. The need
for a sociological conception of AI lies in the missing link
to consistent social sciences empirical studies. Existing
research is predominantly driven by the technical possibil-
ity (machine learning and neural network) applied to social
and economic phenomena rather than being spurred by
theoretically grounded research questions (Liu, 2021). To
be more explicit, in the former case AI systems are more
probably blind to social complexity (inequality, diversity,
power structures) while in the latter sociological possibilities
could inform and drive technological development. Here lies
a strong potential for innovation in the current general dis-
course on AI. We need to elaborate on traditional questions
such as how AI modelling can help develop social theory
and methods as much as how social theoretical models might
contribute to the development of AI.
In the past, following the ebbs and flows of AI develop-
ments in the computer science field, some sociologists tried
to make a proper theoretical framework for a sociological
conception of AI. As for other topics, sociology dealt with
other disciplines’ leftovers. It happened to money whose
nature was exclusively investigated by economics while
sociology was left to deal with residual aspects, such as val-
ues (Collins, 1979). History repeats when AI came along
with a restricted view of sociological competence (Wool-
gar, 1985). Since Margaret Boden’s work (1977), the notion
of ‘social’ uncritically refers not to its nature (genesis) but
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to the uses of AI and their effects, restricting the scope of
a sociological investigation. Yet, Woolgar underlines how
distinctively social human behaviour is by having meaning-
generating capabilities and how boundaries within social
inquiry can be reasserted and pushed forward. More than
systemic explanations based on strict formal models, inter-
pretative sociological approaches can ‘expand and elaborate’
on that distinctiveness (Wolfe, 1991). Humans have minds
that interpret the external reality, beyond the ability to follow
instructions. With a ‘mindful brain’ (Edelman & Mount-
castle, 1978) that software—based on algorithms—cannot
have, they also make sense of reality with the aid of frames
through which they organize social experience. Social
frameworks provide implicit knowledge for understand-
ing and moving in and out of different situations, ignoring
inconsistencies and contradictions, as shown in innovative
sociological work by ethnomethodologists Garfinkel (1967)
and Goffman (1974). Not without critics, computer scien-
tists—like Marvin Minsky as well as Robert Schank and
Robert Abelson with their work on frames and knowledge-
structures in the late Seventies—tried to organize the ‘social
life’ for AI systems. Scripts, plans, and goals are shortcuts to
understand a social situation, providing that implicit back-
ground necessary to ‘understand’ the ‘social setting’ for AI
systems (Schwartz, 1989).
Far from fully explaining the entanglement of concepts
such as society and institutions, agency, and intelligence, it
is sufficient to say that there is a strong need for developing
a sociology for AI. Its usefulness lies on the opportunities to
argue both on the sociological origin as much as the social
impacts of AI.
As we said at the beginning of this section, it is not just
sociology contributing to AI, but also AI to sociology.
Agent-based models, computer programs in which intelli-
gent agents interact with each other in a set environment
based on a defined set of rules, have been used to test social
theories and examine macro-level effects from micro-level
behaviour (Salganick, 2017). Examples of agent-based mod-
els used for sociological work include models on the appear-
ance of modern human behaviour (Powell etal., 2009), costs
of sharing information (Čače & Bryson, 2007), the evalu-
ation of cooperation (Axelrod, 1997), political polarisation
(Stewart etal., 2020) and multiple others. Other than the use
of social simulations, AI techniques can be applied to pro-
vide new means of discovering and evaluating sociological
findings (Molina & Garip, 2019).
Rising challenges
Between the ethics and governance of AI systems,
much research interest has been devoted to their perfor-
mance. However, there have been increasing calls in the
development of the means, both in terms of technical and
socio-legal solutions, to ensure the beneficial use of AI. We
have identified three core challenges which we review in this
section: (i) the opaque nature of machines; (ii) the guarantee
of the respect for human agency and control of our autono-
mous artefacts; and (iii) the link to inequalities both as input
to and output of AI systems.
To a comprehensive social and technical perspective to
thrive, the biggest challenges AI faces are biases and dis-
criminations, no novelties to human history. Intelligent
machines replicate, duplicate, existing inequalities since
they rely on biased dataset to start with. As magnifying
glasses, they automate and amplify existing inequalities. Let
alone legitimation (so far an underestimated dimension), the
degree of discretion given to machines varies to the extent
that they are often referred to as ‘black boxes’ operating
with opacity.
Once more sociological research discovered a relation-
ship between AI systems (and its broad constellation of
related techniques) and inequalities, ‘black boxedness,’ and
opacity. As a hot topic for all disciplines involved (from
law to sociology and computer science), they started to be
questioned, sustaining the demand for technical transpar-
ency and explainability. The quest for less biased AI systems
came along as negative social implications were evident to
the general public. Here, we first face what black box and
opacity mean to transparency and explainability, then we
turn to actual examples of automated inequalities to end with
a scrutiny of the quest for AI for social good.
Black boxes andopacity inAI
Various AI—particularly machine learning—techniques
have been making successes in producing accurate predic-
tions and contributing to the ongoing ‘AI hype’. However,
many such systems remain opaque and obscure by commu-
nicating no understanding of the underlying real-time deci-
sion-making mechanisms (Burrell, 2016; Pasquale, 2015).
Hence, people have been having trouble in understanding
how an algorithm is built and why it produces a precise
output (e.g. a decision on a loan, welfare service, college
acceptance or job promotion). This is either due to design
choice, e.g. economic or social factors, or technical limita-
tions as some of the current most well-performing machine
learning approaches do not lend themselves to explanations
(Ananny & Crawford, 2018).
This black-box nature of intelligent systems makes inter-
action limited and uninformative for the end user. The lack
of sufficient information regarding the emerging behaviour
of a system results in its users creating inaccurate mental
models (Wortham etal., 2017), which in turn may lead to
them placing too much or too little trust in their system (Lee
& See, 2004). Either case, this poses a safety risk as people
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L.Sartori, A.Theodorou
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may disuse or misuse the system and contribute to its cred-
ibility (Kemper & Kolkman, 2019). Furthermore, organisa-
tions have been exploiting the black-box nature of systems
to deny the legal liability of their systems. We have already
seen real-world examples of such practices, e.g. Apple’s
discriminatory credit card system (Nasiripour & Natarajan,
2019), Lufthansa’s anti-monopoly (Bundeskartellamt, 2018),
and others.
In response to these concerns, there has been an increas-
ing push by academics and policymakers to make intelli-
gent systems transparent and explainable (Barredo Arrieta
etal., 2019; Guidotti etal., 2019a, 2019b; Miller, 2019;
Theodorou etal., 2017). The former attribute implies that
the system’s decision-making mechanism is available for
on-demand inspections of its status (Theodorou etal., 2017).
Meanwhile, Explainable AI refers to the system being able
to produce explanations of its behaviour, e.g. communicate
the causes that caused an action (Miller, 2019; Pedreschi,
2019). While progress is being made in the technical chal-
lenges related to algorithmic transparency and explainabil-
ity, there are still open-ended questions that require input
from humanities and social sciences. Theodorou etal.
(2017) state how transparency-related information should
vary depending on the context that it is requested; different
stakeholders, systems, and application domains all have dif-
ferent needs on the amount and way of presenting informa-
tion. Failure to contextualise transparency information may
lead to infobesity, i.e. overloading the user with information.
Bringing back a sociological perspective into AI can help
us not only to identify which information is relevant and
meaningful for which stakeholder, but also when and how
to display it.
In the attempt of opening and closely scrutinizing these
black boxes, social sciences started to develop novel meth-
odologies to debunk and penetrate these computational
systems. Christin (2020) distinguishes three approaches:
algorithmic audits, cultural and historical critiques, and
ethnographic studies. The first relies on statistical analy-
sis of online field experiments and a variety of quantitative
datasets (from facial recognition systems to criminal records
data). Although useful, it was criticized for looking into
technical fixes and, thus, enacting the notion of black box
(Abebe etal., 2020). Nevertheless, auditing algorithms is a
first necessary step into questioning hierarchical classifica-
tion systems, contributing to a greater fairness (D’Ignazio &
Klein, 2020). The second approach is informed by a critical
perspective that relates technological development of the
reproduction of structural social and economic inequali-
ties. While computational systems help to reduce society to
metrics (Mau, 2018), the value of sorting and classification,
commensuration, and standardization, is linked to wider
societal processes such as globalization, surveillance and
rationalization (Fourcade & Healy, 2017; Zuboff, 2019). A
major critique relies on the scant attention to the micro level
of analysis where individuals and institutions might shape
the social construction and acceptance of computational
systems. In tackling previous critiques, the third approach
offers a novel view on how cultural imaginaries, institutional
and organizational traditions are in play at the local level.
Building on Social and Technology Studies, studies have
focused especially on workers and how the computational
turn changed their practices. Gig workers and normal users
have gained increasing awareness of algorithms and AI sys-
tems, adapting their social practices and representations of
on-demand and platform economy (Butcher, 2016; Rosen-
blat, 2017). The ethnographic approach has potential for
showing how organizational culture responds when facing
technology and how people use technology to counterbal-
ance its negative social impacts (Elish & Boyd, 2017). Eth-
nography helps uncover the implicit and understand how
data were cemented into the socio-technical system (Marda
& Narayan, 2021).
Guaranteeing human control
However, we should not consider transparency and explain-
ability as panaceas. The goal of transparency is to sim-
ply provide sufficient information to ensure attribution of
accountability and, therefore, human control (Bryson &
Theodorou, 2019). While the usage of approaches such as
human-in-the-loop (Zanzotto, 2019) can provide human
oversight, the concept of human control goes beyond techni-
cal oversight and instead includes the responsibility that lays
in the development and deployment processes. In fact, socio-
technical frameworks for meaningful human control aimed
for high-risk situations, e.g. autonomous weapons, include
the design and governance layers into what it means to have
effective control (Horowitz & Scharre, 2015; Santoni de Sio
& van den Hoven, 2018). Hence, human control can also
be established through the use of well-established design
practices. These practices may include requirements, design,
and development methodologies, e.g. Design for Value (van
den Hoven, 2005; Van de Poel, 2013) or the use of technical
and ethical standards as means of demonstrating due dili-
gence (Bryson & Winfield, 2018). Sociological knowledge
not only enforces control, but it also offers insights about
the existing structure of inequalities where social relations
and structures, hierarchies, and organizations, are entangled.
Incorporating this knowledge into the decision-making of
key stakeholders and directly into a system’s design, e.g. in
its governance layer, can directly.
An important aspect of human control is our ability to
effectively contest decisions made by a computer, i.e. con-
testability. Our ability to appeal to decisions made for us is
considered a universal human right, while the GDPR makes
explicit mention of algorithmic contestability (European
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Parliament and Council of European Union, 2016). Con-
testing a decision requires much more than an explanation of
the behaviour of the system; it requires us to understand and
review a decision made by a system within the socio-legal
context of where and when it was taken (Aler Tubella etal.,
2020). Developing solutions to enable the contestability of
systems we need to better understand the socio-legal norms
that we want to verify our system’s decision against. These
norms are—as sociological research has shown again and
again (Albright, 2019)—context dependent and are tightly
linked to narratives, as we will see in the next section.
Countering this problem led academic and industrial
research to ‘explain’ or even verify a system’s decisions on
the basis of specific social and ethical grounds: from soci-
etal acceptance to accountability, from individual/collective
trust to fairness, from reducing sources of discrimination
to equality enhancement. As Kasirzadeh (2021) points out
“Because inherently complex and complicated AI ecosys-
tems are connected with various stakeholders, institutions,
values, and norms, the simplicity and locality of an expla-
nation should not be the only virtue sought in the design
of explainable AI”, we need to widen the dominant—as
for today—perspective on AI systems. This is a promising
way to respond to challenges and contain risks. We need to
consider the views of all these stakeholders, as their inter-
pretation of values might be different from one to another
(Aler Tubella etal., 2020). This variety of views is not
unexpected, after all, there is no such thing as ‘universal
morality’ (Turiel, 2002). Instead of trying a ‘one-size-fits-
all’ definition for our ethical, social and cultural values, we
should instead always try to make any definition—and claim
compliance to said definition—explicit and transparent (Aler
Tubella etal., 2019). While computer science can provide
the means to formally represent the interpretations of values,
sociology is needed to help us understand where and when
an interpretation is valid and reliable.
Inequalities
Transparency and explainability are also tightly tied to the
issue of inequality. Research has shown, there is an increas-
ing ‘algorithmic reliance’ for individual’s decisions in eve-
ryday life: from simple decisions, such as which movie to
watch or restaurant to eat at (Paraschakis, 2017), to far more
complex—and arguably important—choices about schools
and universities. (O’Neil, 2016). Furthermore, we have seen
the increasing use of intelligent systems to automate—and
in some cases increase the amount of parameters taken into
consideration—decisions with unequal socio-economic
impact related to consumer credit (Aggarwal, 2020;); urban
mobility (Rosenblat etal., 2017), courts (Larson etal.,
2016), welfare (Eubanks, 2018); health (Obermeyer &
Mullainathan, 2019) or territorial and logistic (Ingold &
Soper, 2016) services.
Yet, if we look at two fundamental structural sources of
inequalities, such as gender and race, we can see evidence
on how AI systems are far from being neutral—let alone
fair (Buolamwini & Gebru, 2018; Benjamin, 2019; Edelman
etal., 2017; Hu, 2017; Kleinberg etal., 2019; Noble, 2018;
Zhang etal., 2021). Although these systems may improve
individual and social welfare (Taddeo & Floridi, 2018),
they also have potential for creating a new social underclass
(Benjamin, 2019) and digital poorhouses (Eubanks, 2018)
through exclusionary intersectional practices (Park & Hum-
phrey, 2019). Biases in AI do not exist in a vacuum nor are
the product of algorithms. They exist in our own culture—
including language—and are then obscured and propagated
through the use of intelligent systems (Caliskan etal., 2017).
Several case studies are emblematic of non-neutral and
discriminatory AI applications such as the Amazon hiring
algorithm (Dastin, 2018) or the Apple credit scoring system
(Methnani etal., 2021). The UK A-level grading fiasco rep-
resents an all-encompassing example of how an AI-system
might be biased in both its inner design and training data.
Back in August 2020, the UK Office of Qualifications and
Examinations Regulation overruled the results of A-level
qualification (that certify school leaving and for university
entrance) because of protests spurred around the country.
To face the challenges brought up by the Pandemic, the UK
Government decided—instead of moving the exams online
or in a socially safe environment—to use an algorithm
to award grades had the student taken the exam. Initially
backed by a broad consensus among the public and policy-
makers, the use of such a grading algorithm soon revealed
its unforeseen consequences. Students contended that the
algorithm was biased: it made use of different sources of
data that tended to underestimate teachers’ individual assess-
ments and to overemphasize the school’s grading history.
As a result, it penalized small schools with less stable dis-
tributions of grades or higher percentages of students with
disadvantaged backgrounds (Clarke, 2020). Focusing on a
predicted algorithm-based grade, the UK fiasco revealed
how past inequalities could be reflected and automated into a
score that requires a wider set of information to fairly assess
educational achievements. The unforeseen social implica-
tions of an AI system operating in a complex ecosystem
are now evident. To contain further fiascos, the failed UK
experiment paves the way to a fruitful collaboration between
technical practitioners and sociologists where technical
(transparency, accountability and responsibility) and social
(structural and educational inequalities) are jointly consid-
ered. As such, all stakeholders involved (in this case stu-
dents, teachers, schools, ministry of education among oth-
ers) are considered balancing needs for human control over
AI systems and demands for equality in the society.
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Overall, biases are unavoidable given the impossibility of
data objectivity (D’Ignazio & Klein, 2020; Dignum, 2019;
Leavy etal. 2020). After all, AI systems are an extension of
our moral agency (Bryson, 2017; Theodorou, 2020) and the
inequalities we naturally embody as social actors belong-
ing to social, economic and political structures (e.g. social
classes). As Caliskan etal. (2017) suggest, the use of AI
does not only bring the risk of automating and magnify-
ing inequalities, but it also offers the opportunity to use
transparency in AI to better identify those biases in hope of
their overcoming, or, at least, counterbalance with respect
to society.
However, if we ever want to reach that stage, we need to
build not only the tools, but also the culture for identifying,
acknowledging, and addressing inequalities and discrimina-
tion in our societies. This culture could start by educating
the AI community into understanding the social impact of
its creations by linking mechanisms to ethics and values and
part of social and technical relations (Theodorou & Dig-
num, 2020). It is about developers’ awareness of AI societal
implications. Not only should there be proper training for
developers, but also for all other stakeholders; including
users and the general public that is indirectly affected by the
technology. In the next section, we look at a broad overview
of the current scenarios citizens usually juggle about.
Narratives andmyths: organizing visions
withconcrete eects
Over the centuries, as any technology has developed, fic-
tional and non-fictional narratives of fears and hopes came
along. Associated with specific characteristics of a technol-
ogy, the dominant narratives have had relevant and tangible
effects. Narratives have the capacity to circulate and self-
reinforce through patterns of repetition. As a specific rep-
resentation of a technology, narratives also act as stories
that brighten up mundane lives of individuals and societies
offering a ‘technological myth’ (Mosco, 2004).
The way in which technologies are portrayed is crucial
for their understanding and reception. Examples within the
fields of domestic appliances (e.g. microwave) genetic modi-
fication or climate change offer interesting case studies for
understanding how narratives could shape and influence
further technological development and adoption as well as
perception and confidence.
Speaking of technology, the nearest relevant example in
time is the Internet. In the first decade of a widespread use,
two influential alternative competing narratives stressed
enthusiastic as much as catastrophic consequences on social
relations (Kraut etal., 2002), sociality and trust (Norris,
2004; Wellman, 2001), work (Sproull & Kiesler, 1991),
psychological well-being (Turkle, 1995) and democracy
(Bimber, 2003). Further back, the telephone (Fischer, 1992)
and the television (Bogart, 1956) shared a similar path when
they first came around.
The same is happening to AI. While the effects on work
(De Stefano, 2019; Frey & Osborne, 2017; GPAI, 2021)
and democracy (Manheim & Kaplan, 2019;Schipper, 2020;
Unver, 2018) have been among the first to catch the eye
of social scientists, many on the wider social, economic,
and political implications of AI are flourishing. As a result,
extreme optimistic or pessimistic narratives could be pushed
to their extremes, negatively impacting the future of AI. To
this end it is important to keep in mind that, as previously
said, AI is not new. For example, the reconstruction of the
rise of AI from the 1950s and to early 1970s by the lens of
a technological myth is telling about the underlying dynam-
ics—among all the protagonists conveyed around a new
emerging socio-technical system: from developers, to jour-
nalists, experts and users (Natale & Ballatore, 2020)—and
their outputs. Despite what the narrative of an “AI winter”
contended, it had continued to exercise a strong impact. As
its technological myth went underground, the presence of
a ‘socio-technical trajectory’ allowed for the creation of
a community organized around a shared narrative of the
future. It happened in the past. Messeri and Vertesi (2015)
showed how the two unflown NASA missions in the Six-
ties were crucial to fastening together the planetary science
community. Similarly, the current hype about AI is strongly
organized around predicting and projecting the future, put-
ting forward claims about potential uses and hypothetical
performances. While reinforcing high expectation narratives
around “an AI race between nations” or even of the devel-
opment of a “superhuman Artificial General Intelligence,”
the hype consolidates some narratives that produce concrete
effects on how AI is developed, funded, used. As a mat-
ter of fact, narratives and myths act as ‘organizing visions’
(Dourish & Bell, 2011) embedded in specific cultural and
institutional contexts. They also serve a performative role as
they could obfuscate the real potentialities of technological
advancements (Elish & Boyd, 2017).
This is why we turn deeper into AI narratives. Cave and
Dihal (2019) synthesize the prevalent hopes and parallel
fears in Western countries in the XX and XXI centuries.
These dichotomies of hopes and fears help in disarticulating
the complex imaginary made of fictional and non-fictional
work about AI. Immortality, ease, gratification, and domi-
nance are hopes that contrast fears like inhumanity, obso-
lescence, alienation and uprising. The former are positively
transformational by nature as opposed to the inner instability
brought about by the latter. As with any other technology,
the first and foremost expectation is to provide healthy and
longer lives while fighting against human decadence with
an engineered and wired body. The second dichotomy ‘ease
versus obsolescence’ put in contrast the human dream of
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A sociotechnical perspective forthefuture ofAI: narratives, inequalities, andhuman control
1 3
Page 7 of 11 4
being free from work and the concrete fear of becoming
useless. If the dream can be traced back to the Iliad (around
800 BC), the latest fear is about automation and AI applied
to industry and services, not to mention the Luddites revolt
against the first industrial mechanical looms. AI systems
could also gratify humans being present and attentive,
friends and lovers. Yet, once technology mediates social
relationships, a sense of alienation arises in both social and
economic spheres, as classic sociologists such as Simmel
and Marx famously argued in the late XIX century. The last
dichotomy expands the concept of power as something that
humankind has always strived for and that the society is
filled with. AI is a tool that could help in gaining and main-
taining a position of power (over other countries or specific
groups of people), whereas as an agent it could take control
over humans.
As context dependent as they might be, narratives are
nonetheless a useful instrument to decipher the cultural
and institutional frameworks in which they are rooted, pro-
duced and consumed. As previously mentioned, narratives,
myths and AI-technology are entangled in such a way that
understanding underlining hopes and fears might support or
avoid a specific future development, public reception, and
regulation. Hopes and fears stand cheek to cheek by jowl of
technical advancements.
Implications ofthedominant narratives
Technologies are embedded within larger social systems and
processes, inscribed with the rules, values and interests of
a typically dominant group. So do narratives. They reveal
important glimpses on the existing structure of inequalities
that, through an increasing reliance on formalized algorith-
mic and AI techniques, could be automated. Demystifying
dominant narratives helps, for example, in breaking this path
for automation through the reproduction of stereotypes. That
is to say, it breaks the traditional loop that sees a dominant
group building its own narrative rooted in ordinary, but often
overlooked, class, gender and racial inequalities.
Through a gendered reading of AI and automata, Adams
(2020) recently contributed to expose some of the self-rein-
forcing power asymmetries reflected in the dominant nar-
ratives. Hopes and fears offer insights on the socio-cultural
schemes about the role of women within different societies.
At the same time, in reproducing dominant schemes, they
contribute to shaping current realities. The choice for femi-
nine voices and names of personal assistants like Siri and
Alexa is enlightening. We want technology to help us while
we keep the control-seat. So far, the solution has lied in turn-
ing to prevalent stereotypes that shape women as reassuring
dedicated subjects for caring (Lerner, 2018) with a ‘total
availability’ (Cross, 2016). Not even a chance ‘to blush’
(Unesco, 2019).
Again on gender, research shows biases in recruitment
because the dedicated AI system was trained on decades-
old datasets where fewer women were applying or promoted
(e.g. Amazon HR in Dastin, 2018). While biases in facial
recognition systems are unable to detect black faces (Buo-
lamwini & Gebru, 2018), language translation reflects rou-
tine activities as gendered (Lee, 2018; Marcelo etal., 2020).
Google Translate initially did not offer gender-specific trans-
lation for languages bearing neutral, like Hungarian or Turk-
ish. All professions were referred to as male while domestic
work was attributed to women. Now, this is addressed in
single-sentence translations by showing the translation for
both genders. Albeit an improvement, the use of neutral
pronouns could promote further inclusivity. At the time of
writing, in multi-sentence paragraphs, the gender-specific
translations still occur. In this direction, addressing some of
the most well-known biases in the AI field promises to bring
about positive effects for research on societal impacts and
implications. Also, a more positive and engaging discussion
could benefit people’s knowledge, perception, and reception
of technological innovation in their daily life.
Previous research highlighted at least three directions
for further reflection. First, there is a disconnect between
narratives and actual science. So far, public knowledge on
what AI consists of and what its implications are is poor
(Royal Society, 2017; Zhang & Zafoe, 2019). Among oth-
ers, one reason is found in the relation between technology
and magic. In a self-reinforcing circle, the former promises
technical efficiency while the latter tells an idealized version
of technology. Both sustain a rich imaginary that tends to
depart from reality.
Second, tales of fear dominate over more positive sce-
narios (Cave & Dihal, 2019; Royal Society, 2019). Although
larger quotas of the population are acknowledging aware-
ness of the potential benefits of AI, negative consequences
catch the eye in the scientific and public debates. At first,
talking about the future of work led to a pessimistic sce-
nario where robots were to steal people’s jobs within a short
period of time (Frey and Osborne revised in 2017 their pre-
vious gloomier predictions). One of the first surveys target-
ing Machine Learning showed that it evoked standard ideas
associated with increased efficiency, accuracy, objectivity.
Yet, concerns about potential physical harms, job substitu-
tion or limiting individual choices were prevalent (Royal
Society, 2017).
Third, plenty of evidence highlights a lack of diversity on
both the production and consumption sides of AI (Brous-
sard, 2018; Zhang etal, 2021). Diversity is missing in the
development of the technology as much as in the design of
AI applications. While the latter are naturally prone to reflect
and reproduce biases and inequalities, lack of ex-ante diver-
sity is not exempted from mimicking inequalities. Data too
needs to be diversified (D’Ignazio & Klein, 2020; Eubanks,
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L.Sartori, A.Theodorou
1 3
4 Page 8 of 11
2018) as limited and poor data lead to reinforcing social and
economic existing biases (Crawford etal., 2016).
The perceived and concrete take that individuals have on
the technology also influences the rise of narratives centered
on responsibility. With regard to AI, discussions on abstract
or long-term effects could lead to narratives detached from
daily life where individuals feel disengaged and not respon-
sible. Moreover, investigating narratives strongly involves
ethics (Ward, 2006) because the way we pursue and repro-
duce our myths and organizing visions has consequences.
There lies the need for a truly human-centered AI that could
support honest narratives for a better development, public
reception and regulation. Whether developers, citizens or
policy makers, more realistic narratives help into projecting
the future of AI.
Overall, tackling ignorance and false fears is a way to bet-
ter address future technical developments, correctly direct
public knowledge, debate, and policy in a fruitful collabora-
tion, bidirectional relation, between sociology and AI.
Conclusions andfuture work
As we have been emphasising in our paper, AI practice is
interdisciplinary by nature and it will benefit from a differ-
ent take on technical interventions. Nor superior nor more
appropriate, technical considerations (such as objectivity,
fairness, and accuracy) should go in parallel with other types
of knowledge useful for social change (Green, 2019). What
issues to face, what data to use and what solutions to imple-
ment are compelling, not old-fashioned, questions.
It is not always a question of efficiency and accuracy,
but also it is about inclusivity by bridging the gap between
technical and social research in AI. In addition to respon-
sibility, AI should be inclusive, built upon quality data that
comprises gender, education, ethnicity, and all of the other
social and economic differences that are sometimes deter-
mining factors for inequality. Quality data not only means to
make it respectful of privacy but to make it inclusive when
it comes to social concerns and purposes. Data allows for
model and algorithms development that need, in turn, to be
closely oversighted through mandatory requirements (Dig-
num etal., 2020). From here, the spread of praises for a
‘New Deal’ on data and its diversification and democratiza-
tion (Benjamin, 2019) while pre-emptive and independent
algorithmic impacts assessment tools are important elements
to consider for regulation.
Once both positive and negative technical potentialities
are considered, there is a chance to overcome optimistic
or pessimistic scenarios linked to the major narratives dis-
cussed earlier. Not only is there a chance to tackle techno-
logical potential flaws, but also the current state of the art
allows for a fresh start and a new narrative about technology.
As argued more than twenty years ago (Suchman etal.,
1999), technologies are social practices that can be assessed
within a specific social and institutional context. As such,
practices with social and environmental impacts (Vinuesa
etal., 2020) have different outcomes and impacts on com-
munities of citizens and workers. There is a potential for
democratization of the internal processes to the AI commu-
nities, but also for equalization of the unbalanced outputs of
current AI systems.
Regardless of the potential benefits, we might be at the
brink of a new AI winter. The overhype around AI ignores
that AI systems currently available are usable not in a far-
away tech laboratory, but in our daily routines at home and
at work. Hopes and fears about benefits and dangers play
a central role in the people, businesses, and public bodies’
adoption. At the same time, to effectively change and open
up the dialog around these narratives, a call for diversity
on the production side is necessary as well. Different peo-
ple think differently about AI. It is extremely important to
bring together alternative frames of thinking—in the com-
munity of developers, business, and citizens—to properly
start reflecting about AI as socio-technical systems in both
social and technical terms.
The relevance of technological myths and narratives
applies to AI as much as previous technologies, especially,
in the current hype bubble. Not only is this relevant for
developers, but also for citizens and politicians. People’s
awareness and trust are crucial for adoption and usage of
new technologies, like it has been for the telephone or the
television. As historian Kranzberg (1986) noted, “technol-
ogy is neither good nor bad, nor is it neutral”. Technological
determinism offers an easy trap to fall into when only devel-
opers participate in the design and construction of AI tech-
nology. Likewise, path dependence and lock-ins tricks eas-
ily fall within the comfort zone of programmers and policy
makers. If we consider AI as a sociotechnical system, we are
to include all participants in the process of construction in a
co-creation approach. Hence, research could also shed light
on the legitimization mechanisms underlying the relation-
ship between social and artificial agents. Since technology is
not any magic, the relevance of narratives in shaping current
realities is a strong call for citizens—with their perceptions
and beliefs—to sit at the table for the future of AI.
To address this lack of knowledge, we are currently work-
ing on a comparative project on Italian and Swedish narra-
tives. Our research project, conducted under the umbrella
of HumanE-AI-Net, aims to provide us with a better under-
standing of how narratives related to AI are formed and their
relationship with the perceived trust, hopes, and fears in the
technology.
Acknowledgements The authors would like to thank HumanE-AI-Net
project, which has received funds from the European Union’s Horizon
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A sociotechnical perspective forthefuture ofAI: narratives, inequalities, andhuman control
1 3
Page 9 of 11 4
2020 research and innovation programme under Grant agreement
952026. Theodorou was also supported by the Knut Och Alice Wal-
lenberg Foundation under Grant 2020:0221.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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