B.1 Social Innovation cha(lle)nging Digital Urban Governance
Certomà, Chiara (Sant'Anna School of Advanced Studies)
The “digital turn” produced new spaces for action and areas of intervention for social innovation practices, by giving rise to a multiplicity of initiatives in which digital social innovators and communities exploit the potential of ICTs to advance creative solutions to social problems and collective needs. In so doing, they advance pathbreaking forms of collective agency that mushrooms and infiltrates social organizations and (government, research and business) institutions; and produce transactive forms of governance characterised by continuous restructuration of power and responsibility relationships between multiple public, private and hybrid actors. Their agency is principally exerted in urban ecosystems, whose relative compactness, combined with a high level of offline and online connectivity, population density, potentially exponential creative contamination, and the tendency towards a progressive decentralization of global governance towards local - albeit interconnected- initiatives, offer ideal conditions in terms of practicality, social acceptability and effectiveness of digital social innovation initiatives.
The session invites critical contributes exploring the impact of digitally-enabled social innovations initiatives for digital urban governance.
At the same time these initiatives, although strongly rooted in urban contexts brings about a number of criticalities, because while favoring the overcoming of (social, cultural, geographical) boundaries, also hide the risk of hybrid confinement (e.g. monopolistic control, polarization of opinions, emergence of filter bubbles, digital dependence). Therefore, stepping beyond the dichotomy between the technology optimism of smart innovation and the radical perspective of the “wisdom of the crowd” discontents, contributes are invited to discuss:
(a) Epistemological challenges, e.g. how can digital social innovation be interpreted in the traditional model of knowledge-production and policy-making; how is it blurring the boundaries between knowledge, science and business; what the status of innovator communities is?
(b) Socio-political challenges, e.g. how is social innovation agency turning traditional multi-stakeholders’ model into a distributed and transactional governance model; how is it reshaping the traditional forms of citizen agency, participation and entrepreneurship; how is social empowerment created, contested and deployed?
(c) Management challenges, e.g. how does digital innovation deal with the controversial shortcomings of a city planned, managed and controlled by highly integrated systems of sentient and interactive technologies; how are critical issues of institutional co-optation, digital exclusions and cyber-segmentation, efficiency and effectiveness addressed; how does innovators community interact with local, national and macro-regional institutions, academics and research institutions, civil society, corporativist organization and intermediate bodies and business?
Contributes exploring further challenges will be also considered as far as they revolve around the exploration of how are social brokers pathbreaking the way for tackling with social challenges via innovative governance processes in the urban context.
KEYWORDS: urban governance, city, digital social innovation, social tech
B.2 Digitalisation of higher education: revolutionising education or the digitalisation of traditional solutions?
Géring, Zsuzsanna, Király, Gábor (Future of Higher Education Research Centre at Budapest Business School)
In relation to the current state of higher education, we can often hear that institutions tend to be quite conservative (attempting to maintain their organisational arrangements, traditions and academic norms etc.), even though they are under pressure to adapt themselves to social, economic and technological changes in order to be more future-proof. At the same time, the widespread availability of professional online content, as well as advances in digital learning solutions, are often represented as very effective and as possible ways to revolutionise the current system of education, both at national and international levels.
However, the underpinning perspective on teaching and learning behind these solutions tends to be rather naive and simplistic. Often they view the main task of education as a mere transfer of information and knowledge, and offer straightforward technological fixes to do it in a more (cost-)effective and less time-consuming way. This one-dimensional understanding of the educational process narrows down the discursive space on issues of teaching and learning, and, in turn, ignores the fact that the educational process can serve several purposes.
If we open up this pre-reflective space for further discussion and shared thinking, we can raise important and topical questions. For example, we can ask what other purposes of education there are beyond knowledge transfer, and how different, alternative forms of socio-technical configurations can support these purposes.
In relation to these general questions, we would like to invite short presentations discussing the following issues:
- How does digitalisation affect the main issues of teaching and learning:
- the content (the what question of education),
- the location (the where question),
- and the method (the how question of education)?
- How do digital solutions shape the role of the lecturer in higher education?
- Do we need to adapt the characteristics of brick-and-mortar higher education (mass education, fixed curricula, group-level interaction, limited availability, bureaucratic structures/relationships and relatively slow processes etc.) to the inherent features of digital society (personalisation, flexibility, speed, promptness, constant availability, interconnectedness, extended network of weak ties)?
As for the structure of the session, we propose an atypical format in order to maximise the opportunities for discussion and foster new relationships between participants. The session shall consist of short (8- to 10-minute) thought-provoking presentations followed by short (10- to 15-minute) small-group discussions on a predefined question. This shall mean that the presenters are asked to focus on their core message(s) while omitting aspects which are less relevant for initiating debate and shared thinking (such as a lengthy description of the theoretical or methodological background). This way the presenters would be able to convey the most important messages of their research, yet still maximise the benefit of meeting with new colleagues and discussing key questions about the effect of digitalisation on higher education.
KEYWORDS: future of higher education, digitalisation of teaching and learning, organisational adaptations
B.3 Putting the Netizen at the Center of Data Protection and Privacy
Kapur, Rajesh (TIMSCDR, Mumbai)
Data has become the driving force of our society, commerce and governance. The proliferation of digital data collection and its analysis has become an inalienable part of human evolution – social, cultural, scientific or technological. In almost all our contemporary interactions, we either seek data, or alternatively, seek to avoid sharing it. Commercial, business and various other organizations - profit as well as non-profit - are using our data to determine how to most effectively engage with us. Furthermore, the relative costs of collection, storage and the processing of data are continuously coming down, resulting in even greater pervasiveness of digital services.
Ensuring data protection and privacy of the individual is the primary concern of any form of data regulation in a civilized society. It has been the central theme of all legislation dealing with the online rights of the individual. The rise of data as the foundation of commerce in the information age has led to concern over data privacy and ethical use of personal data. To address these concerns, enhanced measures of security and trust have been deployed to safeguard the integrity and confidentiality of digital data and communications. These include secure and authenticated access to web browsing, electronic mail, online banking, and critical public services. Some governments however, are concerned that these heightened security measures could make it harder to collect information to prevent or punish terrorists and criminals. They have legislated mandates that give law enforcement and intelligence agencies the authority to intercept private communications. This also poses a threat to online security.
There is a global debate on the type and category of personal and private information that is confidential and that which can be accessed by either the government or corporate entities for commercial gain. This debate gives rise to the following questions:
- How do social and ethical norms emerge vis-à-vis the exploitation of Information and Communication Technologies (ICTs) in social endeavours?
- What are the forces at play? How can their priorities be balanced for maximal benefit to the individual and society?
- How do differing entities start to agree on what is appropriate or inappropriate in individual data collection, storage and dissemination?
- What are the ethical search strategies for commercial and governance imperatives?
- How can technology be used to prevent identity theft, while simultaneously preserving the security and authorized access to electronic data?
KEYWORDS: Data Privacy, Netizen, Data Protections
B.4 Standardisation and the Digital Society
Jakobs, Kai (RWTH Aachen University)
Smart systems are a fairly recent phenomenon that is becoming increasingly popular, not just as a research topic but also in real life. Prominent examples include the Smart Grid, Smart Manufacturing, Intelligent Transport Systems and Smart Cities. The ‘smartness’ of these systems is the result of the integration of ICT into ‘traditional’ technologies, thus providing them with advanced control and communication functions previously available only in the virtual world.
With smart systems, billions of sensors (the ‘Internet of Things’, IoT) will collect data and transmit it for analysis. This process may lead to conclusions, recommendations and, eventually, action, possibly without any human intervention. In any case, the outcomes will have major socio-economic, ecological and potentially ethical ramifications and will directly impact citizens, businesses and society as a whole. They may also have considerable legal ramifications.
This session solicits contributions that may help explain and explore the role of standards and standardisation for the ongoing digitisation of society and how STS could help improve standards setting and to make standards more beneficial for society.
Topics include (but are not limited to):
- The role of standards for the digital society.
- Responsible standardisation.
- The role and representation of societal stakeholders in smart systems standardisation.
- Legitimacy and influence of different players in standards development.
- Potential ethical issues in smart systems standardisation.
- Possible contributions of society to standards development.
- Social norms and their impact on standardisation.
KEYWORDS: Smart Systems, Standardisation, Societal Issuess
B.5 The Digitalisation of Markets and Big Data as a Threat to a Democratic Society
Robertson, Viktoria (University of Graz)
In the 21st century, voter choice and the broader political debate are within the reach of those that can access and channel the vast streams of user data that are generated online. The digitalisation of markets thereby touches upon the very core on which the social contract in liberal democracies is built, namely an open, liberal and democratic society (Popper 1945). In digitalised markets that operate within a digitalised society, the use of big data has the capability to threaten the intrinsic values of a democratic society, including free elections, equal rights, civil liberties, a free press and minority rights. The instances of data-driven influence extending into the political realm are manifold: Political marketing micro-targets voters based on detailed user data. Filter bubbles restrict the range of available information and limit communication to like-minded persons, thereby shaping the public debate and leading to polarisation. Digital assistants (eg, Alexa, Siri) select news items, possibly introducing a bias. Geolocation data may prevent the freedom of assembly. Personal user data from facial recognition in smartphones may be used to undermine civic rights. App stores have discretion over which apps to allow onto their platform, possibly holding back those that political leadership views critically.
The session on “The Digitalization of Markets and Big Data as a Threat to a Democratic Society” will analyse the transformation of markets and the resulting data-driven influences on our democratic society from different points of view. Contributions to this session may include (but are not limited to):
- an investigation into how digital platforms harvest, process and use personal user data, relating these insights to the resulting capabilities of these platforms in a democratic (or non-democratic) society,
- an analysis of how users handle different aspects of data-driven influence on the democratic society (eg, related to free elections, a free press, etc),
- case studies on the political influence of social media platforms in specific cases (eg, Brexit referendum, 2016 US presidential elections, etc),
- a behavioural analysis of social media users and their voting pattern,
- a conceptualisation of democratic influence in the era of big data,
- an assessment of possible legal (eg, data protection law, competitionlaw, etc), political and societal (eg, OneSub, Nuzzera, AllSides) responses to the phenomenon of data-driven influence on a democratic society,
- a comparison of data-driven democratic influence in liberal democracies (eg, EU, US) with the political influence that is exerted thanks to big data in countries based on a different political system (eg, China).
The aim of the session is to facilitate a discussion amongst different disciplines (eg, communication scientists, data scientists, legal scholars, media scholars, political scientists, privacy scholars, sociologists and scholars from science, technology and society studies) on the threats that the digitalisation of markets and big data pose for a democratic society, and develop first ideas on how this may be countered.
KEYWORDS: Data, democratic society, digitalisation of markets, liberal democracy, political influences
B.6 Digital Platforms and the Transformation of Public Communication
Schrape, Jan-Felix (University of Stuttgart)
Intermediary media platforms are not an exclusive phenomenon of the digital age: In the 19th century, the first news agencies were established to collect, process and pass on news to subscribing media outlets; at the same time, the first logistical intermediaries emerged in the bookselling sector; in many respects, newspapers can also be described as intermediary platforms between journalists, advertisers and readers. However, only with the establishment of the Internet and easy-to-use devices, recipients see themselves in a position to access the catalogue of the platforms themselves and to select the content with algorithmic tools—just as all usage dynamics can be aggregated and evaluated.
Thus, on the one hand, ‘platforms’ as a socio-technical coordination structures become the focus of attention; on the other hand, this change results in serious shifts in media economics and in the structures of public communication. In this respect, José van Dijck et al. (2018) aptly speak of an “unbundling” and “rebundling” of content, distribution, advertising and audience. What is decisive here is not only that these dynamics of unbundling or rebundling involve technology-centered companies such as Facebook, Google, Apple and Twitter, that do not feel committed to journalistic standards, but also that the interweaving of very different platforms such as search engines, social media services and media portals goes hand in hand with a changed balance of centralization and decentralization.
Contrary to many expectations, journalism has so far not experienced elementary competition from amateurs or algorithms in the production of news. However, media companies are increasingly losing control over the dissemination of their news, since the central online platforms are becoming the all-decisive intermediaries between content, audience and advertisers. The distribution of news is thus increasingly oriented towards the algorithmically measurable preferences of the users, which can be described to some extent as decentralization of the weighting of news, but can also lead to a polarization of political exchange. Thus, the visibility of content and statements is no longer determined by their reflection in the large media publications, but in a socio-technical interplay between online platforms, advertising networks, media providers and reception dynamics. On the one hand, this goes hand in hand with increased permeability; on the other hand, each platform is characterized by a strong organizational nucleus, which structures the respective communication dynamics.
Against this background, this session aims to explore the potentials and limits of given social-theoretical conceptions of the public sphere:
To what extent do well-established theories continue to offer an informative classification foil in the investigation of public communication in the digital age?
Which novel dynamics of interaction and exchange remain invisible in traditional models of the public sphere?
Which alternative conceptions of networked platform publics have so far proven to be instructive beyond individual case studies?
Do the empirically observable dynamics in contemporary public communication even speak in favor of saying goodbye to ideas of a public sphere as a whole and of starting from multiple arenas of public communication that are at best loosely coupled with one another?
KEYWORDS: digital platforms, public communication, public sphere, news, social theorys
B.7 Artificial Intelligence, Machine Learning and Deep Learning - a Challenge for the Social Studies of Technology (?)
Eggert, Michael (RWTH Aachen), Häußling, Roger (RWTH Aachen), Schmitt, Marco (RWTH Aachen)
New developments in the field of Artificial Intelligence, which are mainly related to the term "Deep Learning", pose enormous challenges for society, but also for the social studies of technology. In addition to questions of control and coupling, which have always been at the center of sociological research on technology, it is now important that social identities increasingly are no longer constructed and determined solely by interaction with other human actors, but that social interactions are searched for patterns by observers of a different kind (AIs) from which relevant social identities can be constructed. Two facets of AI based on deep learning or neural networks are of particular interest here: Firstly, it is the technological use of emergent learning effects that is characterized by limiting control to the input and output of the technology. On the other hand, it is the increasing networking of AI applications through the advancing digitalisation of more and more relevant areas of society. Both aspects contribute to a growing social significance of AI and also to a change in the socio-technical perspective on this phenomenon.
For the Social Studies of Technology, this raises not only empirical questions about the way in which these processes can be adequately investigated, but also theoretical questions about how this type of observation and identity construction can be approached. Relational approaches, such as ANT, practice theories or the more recent developments in network theory, which constructs identities from connections, seem to offer fruitful perspectives here. With this session we want to explore how developments in the field of AI and their social application can be opened up theoretically and empirically by sociological approaches. Which concepts of identity, decision and action prove to be sustainable, how can one gain access to the operations that now produce social identities, and how can one observe the consequences of these identities generated in a new way?
The organizers would like to invite two types of contributions to discuss these questions. On the one hand, we call for contributions that present current research with innovative conceptual or methodological development on questions of sociological analysis of developments in the field of Artificial Intelligence and their increasing application in many social fields. On the other hand, we would like to invite you to a workshop discussion format, which offers the participants the opportunity to discuss specific research problems in this field on the basis of detailed insights into the research process.
KEYWORDS: AI, deep learning, relational sociology, social identities
B.8 Robots and the (Dis)simulation of the Human World
Shin, Heesun (KAIST)
A century ago, the word ‘robot’ was first coined and used by a Czech writer Karel Čapek in his play *R.U.R. (Rossum’s Universal Robots)*. Motivated by the Czech word *robota*, meaning “forced labor,” the word robot has gained exponential popularity worldwide, now used in most languages. Robots, once used only as a term to describe a non-human being that could reduce or replace human labor, are now found in everyday life. They are in factories, hospitals, schools, airports, homes, and even in disaster zones. Or, some are in the laboratories, awaiting their debut.
Despite the wide use of robots and expectation towards them, however, only a small number of studies have examined how they actually shape and are shaped by the political interests, social norms, and cultural practices of the human world. This session draws upon the idea that the development of robots inherently involves the simulation of the human and the human world, in both physical and social sense. The human bodies, relationships, and environments serve as a source of instant and constant inspiration for robotics engineers. At the same time, however, some other parts of the world are intentionally or inadvertently dissimulated for technological, political, and cultural reasons. We take the identification of such discrepancies as the starting point for our discussion.
Based on the idea of robots as ‘situated machines’ within the dynamics of the human world, this session takes a critical approach to the designs, cultures, and the narratives of robots. We invite researches that explore how the development and use of robots simulate or dissimulate human conditions. Questions may include:
- How do designs of the robots simulate (only a certain type of) the human? How do they reflect, reproduce, and reinforce stereotypes of the underrepresented groups?
- For whom are robots developed? Who or what do the designs of the robots include and exclude?
- How do the robotics engineers reduce, reframe, and reconstitute the human world? How are the reinterpreted world represented in the robotic designs?
- How do robots and the robotics culture challenge the traditional concepts of the human world such as labor, care, love, expertise, professionalism, and ethics?
KEYWORDS: Robot, Simulation, Dissimulation, Situated machiness
B.9 Digitalisation and Work: Perils and Promise
Meacham, Darian (University of Maastricht), Gianni, Robert (University of Maastricht)
Changes to the nature of work due to automation and data-driven technologies are a high priority for policy-makers. While new technologies can create new jobs, many jobs are also vulnerable to automation and newly created ones will require combinations of digital and social skills that are in short supply. These changes also threaten to exacerbate job polarization, regional disparity and inequality. Digitalisation in the workplace is also transforming many jobs, at both ends of the wage scale. Managing these transformations to ensure good working conditions should be equally a priority.
The usual focus on (economically) productive work has been also criticized for overlooking all those forms of non-productive work that underpin society. With the emergence of automatization, these jobs, that are often related to care will assume a different relevance.
The radical changes that are forecasted raise several questions related to the impact of digitalisation on labour and the consequences for social interactions.
What are the negative and positive aspects in adopting data-driven technologies? If work performed by technologies might be more accurate and faster how can we address issues of responsibility and accountability related to their results? Where polarization threatens to hollow out middle income jobs, are alternative measures of welfare sufficient to replace the potential loss of meaning of work for individuals in their interactions?
These questions cannot be addressed only through to an academic debate but require a broader societal involvement to understand hidden aspects and measures to mitigate potentially negative outcomes.
In the last decade in Europe the discussion over ethical aspects related to emerging technologies has become stronger than ever. Accordingly, researchers and policy-makers are seeking for different methodologies to evaluate these issues and to enlarge the debate also to other actors concerned with the changes brought by emerging technologies. Responsible Research and Innovation for instance, aims at covering the different levels of engagement to address the issues arising from specific contexts and their connection with the overall modifications of social dynamics. However, it is still unclear how RRI could be implemented in order to tackle the challenges related to the adoption of data-driven technologies and its consequences for the labour process.
How can stakeholders work together to ensure a just transition and implementation of data-driven technologies? What understanding of responsibility can better link aspects of efficiency with those of legitimacy? Do we need to steer digitalisation towards democratic features in order to protect vulnerable actors? What measures can realistically be helpful in order to achieve such objective?
The session welcomes contribution addressing these questions and the overall relation between digitalisation of work and democratic/responsible processes. These include but are not limited to the objectives of digitalisation, the impact of digitalisation on work, the methodologies to address the potential impact of digitalisation and the different ethics framework useful to evaluate their role. Besides, the session will welcome papers discussing the concepts of responsibility and vulnerability in relation to digitalisation and work. Contributions providing specific models of implementation for RRI and digitalisation are also particularly welcomed.
KEYWORDS: Digitalisation; Work; Data-Driven Technologies; RRIs
B.10 Models, Simulations & Algorithms - Policy Support in the Digital Age
Bauer, Anja (Alpen-Adria University Klagenfurt), Capari, Leo (Institute of Technology Assessment of the Austrian Academy of Sciences), Fuchs, Daniela (Institute of Technology Assessment of the Austrian Academy of Sciences), Udrea, Titus (Institute of Technology Assessment of the Austrian Academy of Sciences)
Just recently, in November 2019, the European Commission organized its first-ever conference on modelling for policy support, inviting experts, politicians and stakeholders to discuss respective experiences, challenges and trends. This event illustrates the increasing importance of computer models in informing and guiding policies, from the identification and analysis of societal problems to the examination of different policy instruments and the assessment of the costs and impacts of planned and implemented policies. For example, computer models are used in risk governance to determine the toxicity of chemicals, computer simulations inform political strategies for energy transitions on EU, national and regional levels and computer models suggest potential effects of preferential trade agreements between countries. Technical improvements - both with regard to data basis as well as enhanced computing power - allow for the development and calculation of ever more complex models. Yet, models are not uncontested: The most prominent example might be the accusation of the politicization of IPCC climate models. Moreover, questions of transparency, interpretability and informative values remain challenging for both, modelers and decision-makers. Recently, debates on computational modelling in policy advice have gained new impetus by advances in Artificial Intelligence (AI) and Big Data. Based on abundant and just-in time data, learning machines are tasked with developing models for the prediction and management of a variety of situations, be it the occurrences of crimes or terrorist attacks, the most efficient use of social assistance or the identification of tax fraud. Such developments have stimulated public concerns about the biases and power of algorithms and the risks of autonomous decision-making for democracies.
In this session, we aim at exploring how the increasing digitization affects practices of policy support (from advice in policy formulation, the assessment of policy options to the bureaucratic implementation of policy measures), and in a broader view, societal decision-making and democracy. We invite presentations along the following themes and questions:
- (Hidden) assumptions, values and biases in models and algorithms: Which and whose assumptions and values are (intentionally or unintentionally) inscribed in models and algorithms – and therefore remain unchallenged in broader discourses - and how? What biases are produced or maintained and how can they be disclosed?
- Models as boundary objects between science and politics: How are models, their assumptions and results negotiated and communicated between different Societal actors? How is the scientific and political authority of models enacted at the science-policy interface?
- Models and democracy: What opportunities and barriers do models provide for democratic deliberations of future visions? How are different societal groups and their perspectives included in modelling practices? Do new developments in AI, Big Data and machine learning foster technocratic or democratic tendencies in policy-making?
- Models and transparency: How does the increasing use of modelling for policy support relate to demands for transparency? How can accountability and transparency of policy advice and decision-making processes be ensured in the view of ever more complex models and algorithms?
KEYWORDS: computer model, simulation, algorithm, policy support, decision-makings
B.11 Algorithmic inequality. Intersectional divisions in the digital society
Lopez, Paola (University of Vienna), Eyert, Florian (Weizenbaum Institute)
In recent years we have seen a new wave of politicization of digital technology, from the critique of the monopoly positions of digital platform companies (Srnicek 2016) to the interdisciplinary examinations of unfair and opaque algorithmic decision-making systems (Angwin et al. 2016, Chouldechova 2017). As ubiquitous computing environments, machine learning based prediction systems and the platformization of economic and political life are advancing into more and more aspects of society, a myriad of new ways of interactions and entanglements with dynamics of social exclusion and systemic inequality are evolving. Examples of the many pressing issues are race and gender biases in facial recognition systems (Buolamwini/Gebru 2018), new economic insecurities arising from the spread of crowd work (Gerber/Krzywdzinski 2019), discrimination embedded in the prediction systems used in welfare allocation (Eubanks 2018, Lopez 2019) and the amplification of existing disparities trough feedback loops in predictive policing (Ensign et al. 2018).
There is a long tradition in STS to analyze the relation between structural inequalities and scientific and technological practices (e.g. Hess et al. 2017) and the challenge now lies in updating them to grasp the current shifts in power structures and inequalities implied by digital technologies guiding various governance and coordination practices. For instance, the wide range of works on classification and inequality in STS (Bowker/Star 2000) can be transferred to present data-driven techniques, and the emerging scholarship on digital STS (Vertesi/Ribes 2019) promises fruitful perspectives on social inequalities. In order to strengthen a genuine STS perspective on the new configurations of intersectional inequalities arising from the current phase of the digital transformation, we invite contributions that address some of the following questions:
- How and where do digital artifacts and practices exacerbate, reinforce, mitigate or transform existing inequalities?
- Which kinds of reconfigurations of intersecting axes of gendered, racialized and economic vulnerabilities can be observed or are to be expected in the near future?
- Which new dimensions of structural inequality are emerging from the role of digital technology in our current societies, and which existing dimensions are merely rendered visible by digital technologies?
- In which ways do existing technologies that originally aimed to counteract individual human biases actually amplify existing societal biases?
- How does the very materiality of data-based infrastructures relate to global inequalities?
- How can former discourses around a "digital divide" and more modern surveillance discourses be brought together, showing that visibility, as well as representation, is always ambivalent?
- How can perspectives like xenofeminism (Hester 2018) and concepts like surveillance capitalism (Zuboff 2019) be integrated into science and technology studies?
- How can technical terms related to the modern, probabilistic AI paradigm, such as "accuracy" and "prediction", be approached through a critical STS analysis?
- How can we conceptually grasp the individualization of group-based inequalities through the platform user paradigm of "personalization" and which ramifications are arising for social justice movements?
- How can we use established perspectives and methodologies to study digital inequalities and which new analytical lenses need to be developed?
KEYWORDS: Digitalization, social inequality, algorithmic governance, intersectional analysis, fairness in machine learning