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Towards a Sociological Conception
of Artificial Intelligence
Jakub Mlynář1,2[0000-0001-5206-3212], Hamed S. Alavi2,3[0000-0001-8443-7514], Himanshu
Verma2[0000-0002-2494-1556] and Lorenzo Cantoni4[0000-0001-5644-6501]
1 Charles University, Nám. Jana Palacha 2, 116 38 Praha, Czech Republic
2 University of Fribourg, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland
3 University College London, 66-72 Gower Street, WC1E 6EA London, United Kingdom
4 University of Lugano, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
jakub.mlynar@ff.cuni.cz
h.alavi@ucl.ac.uk
himanshu.verma@unifr.ch
lorenzo.cantoni@usi.ch
Abstract. Social sciences have been always formed and influenced by the
development of society, adjusting the conceptual, methodological, and
theoretical frameworks to emerging social phenomena. In recent years, with the
leap in the advancement of Artificial Intelligence (AI) and the proliferation of
its everyday applications, “non-human intelligent actors” are increasingly
becoming part of the society. This is manifested in the evolving realms of smart
home systems, autonomous vehicles, chatbots, intelligent public displays, etc.
In this paper, we present a prospective research project that takes one of the
pioneering steps towards establishing a “distinctively sociological” conception
of AI. Its first objective is to extract the existing conceptions of AI as perceived
by its technological developers and (possibly differently) by its users. In the
second part, capitalizing on a set of interviews with experts from social science
domains, we will explore the new imaginable conceptions of AI that do not
originate from its technological possibilities but rather from societal necessities.
The current formal ways of defining AI are grounded in the technological
possibilities, namely machine learning methods and neural network models. But
what exactly is AI as a social phenomenon, which may act on its own, can be
blamed responsible for ethically problematic behavior, or even endanger
people’s employment? We argue that such conceptual investigation is a crucial
step for further empirical studies of phenomena related to AI’s position in
current societies, but also will open up ways for critiques of new technological
advancements with social consequences in mind from the outset.
Keywords: Artificial Intelligence, Sociology, Social Sciences.
1 AI as a Sociological Phenomenon
Given the rapidly growing importance of Artificial Intelligence (AI) in many domains
of social life, it is striking that the interest of sociologists and social scientists in AI
has been quite scarce. At the end of 20th century, AI was occasionally discussed in
2
sociology as a methodological tool for data analysis and theory development, yet not
as a social phenomenon in its own right. However, as it is expected that the social
impact of AI will continue to increase over the next years, contributing to transform
the ways people organize economical production, learn and spend leisure time, to
name just a few concerned fields, we argue that sociology and other social sciences
need to acquire an adequate understanding of how artificial intelligence is and should
be grown into a social actor, reflecting its relevance and consequentiality in different
layers of social organization and social reality.
This precisely is the central aim of our project, which we describe in this paper. It
intends to provide a sociological conception of AI, i.e. understanding AI as a social
phenomenon
and a non-human social actor
. We are convinced that scientific studies
of human beings and their collectivities need to tailor their conceptual tools to the
society of the 21st century. This requires, first and foremost, a proper understanding
of specific aspects of digitalization in everyday life, which is a domain where AI
plays an increasing role. Through an exploratory study, our intention is to “prototype“
the methodological and conceptual cross-fertilization between sociology and the
fields that deal traditionally with the subject of AI, such as computer science,
philosophy of mind, and cognitive psychology. The second equally important goal of
our project is to execute an investigation of the possibilities of sociology to influence
and guide the future of AI-related developments in our societies. In this paper, rather
than presenting results of empirical research, we introduce and discuss an agenda to
proceed.
1.1 The Case of Facebook
In the recent public and political discourses, the role of AI has been already the
subject of challenging debates. In the US Senate hearing held on April 10th, 2018,
with Mark Zuckerberg, the founder and CEO of Facebook, the words “Artificial
Intelligence” or their abbreviation “A.I.” were used 29 times. In fact, more often than
“trust” (20 occurrences), “transparency” and “transparent” (18 occ.), or even
“freedom(s)” (4 occ.) and “democracy/democratic” (3 occ.). This was also noticed by
the participants of the meeting, one of whom noted that Zuckerberg “brought [AI] up
many times during [his] testimony.” (Senator Peters, 3:47:13-3:47:16 of Zuckerberg’s
US Senate hearing as available online ) This – highly medially exposed – example
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documents and illustrates the role that AI has taken in current societies worldwide. It
also points to the major role that the AI “systems” or “tools” might play in future
social developments. Indeed, Zuckerberg himself stressed the societal relevance of
such questions more than once during the hearing session: “[A]s we’re able to
technologically shift towards especially having AI proactively look at content, I think
that that’s going to create massive questions for society about what obligations we
want to require companies ... to fulfill.” (Mark Zuckerberg, 2:53:48-2:54:05 of the
hearing) And also about one hour later: “[T]he core question you’re asking about, AI
transparency, is a really important one that people are just starting to very seriously
1 https://www.youtube.com/watch?v=pXq-5L2ghhg
3
study, and that’s ramping up a lot. And I think this is going to be a very central
question for how we think about AI systems over the next decade and beyond.” (Mark
Zuckerberg, 3:47:46-3:48:02 of the hearing)
1.2 Our Approach
In providing a sociological conception of AI, our project starts from an extensive
literature review, as well as content analysis of media production related to AI. At the
most fundamental level, we aim to conceptualize AI sociologically, providing answers
to questions such as: Are there inherent differences between human and non-human
(AI) social actors? Should we revisit and reconsider our presumptions of human
uniqueness? And, on the other hand, can we truly speak about anything like “AI in
general”, or do we rather encounter loosely related instances of phenomena in the
sense of “family resemblances” [1]? As explained further in this paper, the answers
provided will be based on analysis of several kinds of empirical data, qualitative and
quantitative in nature. The resulting conception of AI, although sociological in its
nature, will be then adaptable by other social sciences such as communication,
economy, political science, or social anthropology.
2 The Study of AI in Sociology and Computer Science
2.1 AI and Non-Human Actors in Sociology
With the growth of initiatives such as Ubiquitous Computing (UbiComp
, see e.g. [2]),
and the remarkable entry of “smart” systems into the domain of everyday social lives,
it is necessary to reconsider the position of AI in sociology and vice versa
.
Historically and traditionally, sociology was usually practiced – as Zygmunt Bauman
once nicely put it – as “a narrative on what follows from the fact that man is not
alone” [3]. One of the tacit presumptions, arising from this conception of sociology as
a science on accumulated and interrelated human beings, has been the disregard for
non-human actors and material components of the social world (cf. [4]). Sociologists
simply considered the “non-human“ and “extra-human” to compose only the
environment of sociologically relevant phenomena, which does not have to be taken
into account. Since the late 1970s, this neglect was explicitly formulated and criticised
in sociological orientation to subjects such as the natural environment [5], animals [6],
or technology [7]. Focusing specifically on AI – which has been extensively discussed
in cognitive psychology, philosophy of mind, and computer science already since the
1950s (cf. [8]) –, few sociologists have started writing on the subject in the 1980s and
1990s. However, up to this day, AI has been almost exclusively conceived in
sociological context only as a methodological tool in statistical or textual analysis [9],
and development of sociological theories [10] – in other words, an “application of
machine intelligence techniques to social phenomena”, i.e. the Artificial Social
Intelligence [11].
4
Broadly speaking, so far, AI has not been systematically considered as a social and
sociological phenomenon sui generis and the discipline of sociology lacks a suitable
conception of AI, which could serve as a framework for empirical studies. Rare
exceptions include that of Woolgar [12], who proposed a “sociology of machines”,
arguing that we should see the “AI phenomenon as an occasion for reassessing the
central axiom of sociology that there is something distinctively ‘social’ about human
behaviour” (p. 557), and proposed that we should examine the underlying assumption
in social sciences that there is a fundamental difference between humans and
machines (and, by extension, also between human and machine intelligence). Apart
from advocating sociological research of AI discourse and AI research practices, he
also claimed more broadly that “the phenomenon of AI provides an opportunity for
investigating how presumptions of the distinction between human and machine
delimit social inquiry” (p. 568). Wolfe [13] explored Woolgar’s radical idea and
demonstrated that “interpretive” sociological approaches (such as ethnomethodology
or symbolic interactionism), rather than “systemic” ones, may “expand and elaborate”
the hypothesis of human uniqueness in comparison with AI. Schwartz [14] suggested
that AI has to be studied with regard to the social context (setting) in which it is
“implemented”, and characterized AI systems as “social actors playing social roles”
(p. 199). At the turn of the century, Malsch [15] discussed the proximities of AI and
sociology through the concept of socionics. This field, standing at the intersection of
sociology and AI, aims to “address the question how to exploit models from the social
world for the development of intelligent computer technologies” (p. 155), exploring
the specificities of modern societies and resilient adaptability of social systems in
order to provide means of translating these features into computer-based technologies.
Indeed, the most influential attempt to incorporate non-human actors into sociological
thinking is the conception of Bruno Latour [16]. His actor network theory (ANT)
aims, among other things, to transgress the distinction of human and non-human
actors ([17]; similarly to Woolgar’s [12] argument presented above), acknowledging
technologies and objects as partakers in the construction of society. However, AI as a
phenomenon is not discussed by Latour in this context. More recently, Muhle [18]
presents an ethnomethodological study of “embodied conversational agents” (bots) in
the virtual world of a massively multiplayer online game Second Life
, posing the
question whether bots (i.e. non-playable characters) in computer games are conceived
by players as social actors. His approach relates closely to our own interests, but our
aim is to provide much broader picture. Some other empirical studies of specific
instances involving AI-based technologies have been conducted (such as the use of
smartphones in social interaction: e.g., [19]), however, they rather focus on the
“human side” of the interaction, and without the intention of providing a generalizable
sociological framework of the subject of AI. This is also the case of the field of
Human-AI Interaction, which we review in more detail in the next subsection.
2.2 Human-AI Interaction in Computer Science
Human-AI Interaction, as a field of study, is a subdomain of Human-Computer
Interaction (HCI), and focuses on the understanding of the nuances of our interactions
5
with AI supported tools, technologies, and processes. Although currently in a nascent
stage of development, this subdomain of computer science embodies an extensive
range of contexts, activities, and types of users. Furthermore, the encapsulation of
human-like behavior in artifacts and environments, and embodiment of intelligence in
varied kinds of technologies are being homogenized within the fabric of everyday life.
From domain experts (such as medical experts diagnosing cancer cells through
intelligent image processing [20]) to children (the use of AI in education to improve
the learning experience and outcome [21]) to building and urban dwellers (home
automation controlling thermal comfort of inhabitants [22], and autonomous cars
changing the shape of cities and experience of mobility [23]) to disabled users (for
example mobile assistive applications helping blind users navigate in urban
environments [24–25]), the role of AI in our socio-cultural aspects has become
increasingly pervasive. This is no longer limited to the embodiment of technologies
by artifacts, but also extends to the realm of built environments [26], having direct
spatial and consequently social impacts – topics that the proliferating HCI
contributions in built environments have recently begun to address [27–28]. Still the
conception, design, and study of Human-AI Interaction is predominantly focused on
ad hoc instances
(such as robots, driverless cars, chatbots, etc.) with little or no
overlap between instances of different kinds. This lack of generalizability in the study
of Human-AI Interaction can be attributed to the HCI’s emphasis on design instances
and a bi-directional disconnect between these instances and theoretical frameworks.
In addition, the “black box” approach of representing AI algorithms by the
researchers oftentimes undermines the efforts to achieve a significant level of
generalizability. Consequently, the widely accepted conceptions of AI algorithms and
tools, especially their social impact, is currently distributed across the nature and form
of design instances (or products), and how experts and users likewise ascribe meaning
to these separate instances. This multi-layered gap in conceptions about AI and its
societal impact amongst actors of different backgrounds (AI developers, sociologists,
and users), and their varying levels of interactivity with smart technologies has
remained out of the scope of Human-AI Interaction as domain of computer science
research.
2.3 Research Gap
As demonstrated in the previous subsections, there is a research gap in contemporary
sociology as well as computer science which relates to (1) the conceptual
understanding of AI as a specific social (non-human) actor, and (2) the role sociology
could play not only in interpreting but also in helping to lead the future technological
development of AI-based tools, systems and devices. This research gap manifests
itself on several levels of social scientific endeavors: at the level of sociological
theory
, where non-human social actors are commonly “theorized out of existence”,
and also at the level of empirical studies
(similarly to the related domain of
Human-AI Interaction), where the broader societal impacts of AI are not considered,
given the primary focus on particular cases of specific technology use.
6
3 Research Plan and Methodology
3.1 Literature and Discourse Analysis
The first step of our research project will cover an investigation of past and ongoing
discourses within the other relevant research domains pertaining to AI, such as
computer science, cognitive psychology, and philosophy. In particular, we will aim to
identify aspects that are relevant for a specifically sociological formulation of
empirically investigatable research questions related to AI: its societal roles, functions
and imaginaries. In addition, content and discourse analysis of online discussion
forums and other media (TV, newspaper) will be an important initial step in outlining
and understanding the common-sense conceptualizations of AI in current society.
Qualitative and quantitative techniques of content analysis (cf. [29]) will allow us to
systematically gather initial knowledge of the existing imaginaries and
conceptualizations of AI in media. Furthermore, the discursive aspects of the analysed
texts will be studied by the methodologies of discourse analysis [30]. In addition to
literature review, these approaches will serve to further elaborate and specify the
research questions for empirical investigation in the next stages.
3.2 Online Survey
An online survey will aim to collect the widespread common-sense conceptions and
imaginaries of AI in contemporary Swiss society, and capture its expectable varieties.
Our aim is to have a representative sample, reflecting the demographic and social
diversity, and cover all Swiss languages. We will use TypeForm
platform for online
collection of survey data, and distribute the questionnaire among potential
respondents by a number of diverse venues. Descriptive and inferential statistics will
be used to gain quantitative insights and test hypotheses about different manifestation
of certain ideas and their correlation in the survey responses. In addition, to extract the
influence of different variable (culture, age, education level, etc.) on the perception of
AI, we will use exploratory data mining and statistical methods that allow for
clustering and pattern recognition. Visualizing the patterns and quantifying the
seminal components in the current perception of AI will be followed by qualitative
analysis to extract the meanings and nuances of what AI means in our current
societies.
3.3 Observational Studies
In the third phase, we will conduct three in-depth observational studies, collecting
video recordings of instances when a group of individuals interact with AI-based tools
and systems: (i) driverless shuttle; (ii) chatbots; (iii) game-play systems. In order to
extract features of situated common-sense conception of AI from the recordings, we
will analyse the data from the perspective of ethnomethodology and conversation
7
analysis [31–33], which focuses on the “perspective of the actor” and aims to describe
and elucidate the methodical work of practical sense-making in specific social
settings. It has been convincingly demonstrated by previous research in the field that
ethnomethodological analysis of video recordings of social interaction can yield
valuable insights into the details of situated action (e.g., [34]).
3.4 Interviews with Experts
We will conduct approximately 15 semi-structured interviews with experts on AI, as
well as experts in the relevant domains of sociology. First, mostly with the AI experts,
the goal of the interviews will be to discuss the results of empirical studies (see
subsections 3.2 and 3.3) and compare the common-sense conceptions/imaginaries of
AI with the experts’ perspective. In this case, the expert opinions are needed as a
contrastive foil in further elaboration of a truly sociological conception of AI, which
is the ultimate goal of our project. Second, mostly with the social scientists, we will
discuss the possibilities of sociology (or other social sciences) to influence and guide
the future of AI-related developments in our societies. In this case, the expert opinions
are needed especially because of the long-lasting controversies in sociology regarding
social advocacy and public engagement (cf. [33]). As a complementary method of
gathering experts’ opinions, we will also consider using the Delphi method.
3.5 Sociological Conception of AI
The goal of this final phase is to synthesize the main results of all previous phases in a
sociological conception of AI. The conception, we expect, will have the form of an
original coordinate system (matrix) for evaluation of societal conceptions and
imaginaries of AI. We will also conduct a general assessment of the current status of
AI in sociological thought and research. Our theoretical (concept-building) work will
be oriented by two main regards: (1) to sociological research, i.e., the
operationalizability of our conception in further empirical studies conducted from
different paradigmatic standpoints; (2) to other human and social sciences, i.e.,
providing the conceptual framework for sociologically sensitive research in
communication, political science, cultural anthropology, social psychology etc.
4 Conclusion
4.1 Subsequent Research Prospects
A number of empirical studies (qualitative and quantitative) can be outlined as a
direct result of a sociological conceptualization of AI. Our investigation respecifies
and opens up novel fields for collaboration between human/social and
computer/natural sciences. A sociological conceptualization of AI is necessary in
order to carry out further empirical research in this area, which would investigate AI
as a social phenomenon, its imaginaries in different segments of current societies, and
8
the role it has as a non-human social actor in particular social and institutional
settings. Presently, AI is already being applied in a great number of fields, such as
games, households, education, transportation, logistics, industrial production,
marketing and sales, communication, scientific research, data analysis, and many
others. Each of these fields requires sociological knowledge in order to understand AI
application, its impact on “users”, “customers”, “clients”, and their possible concerns
regarding interaction with AI. The ongoing fourth industrial revolution
– expansion of
cyber-physical systems such as AI and robots – will presumably contribute to major
transformations when it comes to the ways we live, think and communicate. Proper
sociological understanding of AI provides us with a historically unique opportunity of
capturing the details of this revolution continuously and progressively as it happens.
The specific subsequent research prospects include survey-based studies of the
diversity of AI imaginaries in different segments of societies (defined economically,
culturally, politically, demographically etc.); detailed examination of communicative
and discursive processes related to AI (such as interaction with chatbots);
investigation of positive and negative impacts of AI-based automation in various
industrial spheres, and its influence on employment; historically oriented explorations
of the image of AI in popular culture; and indeed, further refinement of conceptual
and theoretical frameworks of AI based on empirical validations of sociological
models.
4.2 Innovation Potential
Our project will open the way for sociology and the social sciences into major AI
projects, where their influence is currently only marginal. In particular, sociology and
the social sciences, equipped with an adequate conception of AI, could (and should)
fully contribute to steer the development of AI-based technologies. This is important
especially since the current development of AI is predominantly grounded in the field
of technological possibilities (such as machine learning methods, neural network
models), rather than preliminary consideration of societal effects of the proliferation
and expansion of AI. On the other hand, indeed as with other technologies, it is
2
important to develop AI-based devices and tools in a way that builds on already
existing ways of practical usage of technology. For, as Harvey Sacks remarked
already in the 1960s, any novel technological object is “made at home in the world
that has whatever organization it already has” [33] – it is incorporated in familiar
social practices. We do not need to stress that it is primarily sociology that sets out the
detailed study of the organization of the social world and related practical activities as
its principal and primordial field of interest. Similarly to other domains of technology,
sociology can provide crucial knowledge to AI designers; however, in order to do so,
it needs an appropriate understanding of the subject in question, in our case, artificial
intelligence.
2Our arguments to consider only the currently employed Machine Learning and Neural
Network algorithms rather than Agent-Based and BDI systems in the sociological conceptions
of AI are based on the predominant proliferation of the former methods in intelligent
applications which users often interact with.
9
To conclude, we firmly believe that precise sociological conceptualization of AI
could, in a long-term perspective, improve our comprehension of the nature of
humans and technology. Therefore, sociological conceptualization of AI, and
empirical studies in the sense outlined above, would have far-reaching impact not
only in the field of sociology, but also in human and social sciences in general.
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