Although overarching if not foundational conceptualizations of digital governance in the field of critical data studies aptly account for and explain subjection, calculated resistance is left conceptually unattended despite case studies that document instances of resistance. I ask at the outset why conceptualizations of digital governance are so bleak, and I argue that all are underscored implicitly by a Deleuzian theory of desire that overlooks agency, defined here in Foucauldian terms. I subsequently conceptualize digital governance as encompassing subjection as well (...) as resistance, and I cast the two in relational perspective by making use of the concepts “affordance” and “assemblage” in conjunction with multiple subjectivities and Foucault's view of power as productive as well as his view of resistance as an “antagonism of strategies” in his late scholarship on resistance, ethics, and subjectivity. I offer examples of salient modes of what I call “productive” resistance, and from a Foucauldian perspective I explain how each mode targets and subverts technologies of repressive power to produce new elements of the digital environment and construct new truths. I conclude by recognizing the agency embodied in resistance as an end in itself, but I also consider that modes of productive resistance can have extrinsic value as they affect the fluid interaction among elements of the digital environment, potentially disrupting the presumed structure of dominance and dependence, and opening our conceptualization of algorithmic life to hopeful possibilities for change. (shrink)

Since the inception of recorded music there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labour. With the advent of algorithms, big data and machine learning, loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio (...) mastering processes and radically disrupting mastering careers. The emergence of dedicated products and services in artificial intelligence-driven audio mastering poses profound questions for the future of the music industry, already having faced significant challenges due to the digitalization of music over the past decades. The research reports on qualitative and ethnographic inquiry with audio mastering engineers on the automation of their expertise and the potential for artificial intelligence to augment or replace aspects of their workflows. Investigating audio mastering engineers' awareness of artificial intelligence, the research probes the importance of criticality in their labour. The research identifies intuitive performance and critical listening as areas where human ingenuity and communication pose problems for simulation. Affective labour disrupts speculation of algorithmic domination by highlighting the pragmatic strategies available for humans to adapt and augment digital technologies. (shrink)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

In analyzing oppressive systems like racism, social theorists have articulated accounts of the dynamic interaction and mutual dependence between psychological components, such as individuals’ patterns of thought and action, and social components, such as formal institutions and informal interactions. We argue for the further inclusion of physical components, such as material artifacts and spatial environments. Drawing on socially situated and ecologically embedded approaches in the cognitive sciences, we argue that physical components of racism are not only shaped by, but also (...) shape psychological and social components of racism. Indeed, while our initial focus is on racism and racist things, we contend that our framework is also applicable to other oppressive systems, including sexism, classism, and ableism. This is because racist things are part of a broader class of oppressive things, which are material artifacts and spatial environments that are in congruence with an oppressive system. (shrink)

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly connected to a social (...) justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: visibility, engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract. (shrink)

The emergence of Big Data has added a new aspect to conceptualizing the use of digital technologies in the delivery of public services and for realizing digital governance. This article explores, via the ‘value-chain’ approach, the evolution of digital governance research, and aligns it with current developments associated with data analytics, often referred to as ‘Big Data’. In many ways, the current discourse around Big Data reiterates and repeats established commentaries within the eGovernment research community. This body of knowledge provides (...) an opportunity to reflect on the ‘promise’ of Big Data, both in relation to service delivery and policy formulation. This includes, issues associated with the quality and reliability of data, from mixing public and private sector data, issues associated with the ownership of raw and manipulated data, and ethical issues concerning surveillance and privacy. These insights and the issues raised help assess the value of Big Data in government and smart city environments. (shrink)

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition (...) of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life. (shrink)

The Covid-19 pandemic broke on a world whose grip on epistemic trust was already in disarray. The first months of the pandemic saw many governments publicly performing reliance on epidemiological and modelling expertise in order to signal that data would be the basis for justifying whatever population-level measures of control were judged necessary. But comprehensive data has not become available, and instead scientists, policymakers and the public find themselves in a situation where policy inputs determine the data available and vice (...) versa. This essay asks how we can live with what Amoore has termed ‘post-Cartesian doubt’ in situations of existential risk, and what kind of approach to science and data can answer the moral and human demands of a situation such as the Covid-19 pandemic. I suggest that science and policy could be able to control the pandemic better by addressing the sources of uncertainty and missing data not as gaps in the information landscape, but as individuals who are likely to be members of less-visible and less powerful groups including low-wage workers, the elderly, migrants, prisoners and others. This would shift both data use and policy toward an ethics of care, an embodied approach which asks what people need and how they behave in relation to each other, rather than how to manage population-level behaviour. This approach, I argue, is more appropriate for pandemic response than a utilitarian calculation of how many people each country should expect to lose as a result of the disease. (shrink)

This article examines discourses about mixed reality as a data-rich sensing technology – specifically, engaging with discourses of time as framed by developers, engineers and in corporate PR and marketing in a range of public facing materials. We focus on four main settings in which mixed reality is imagined to be used, and in which time was a dominant discursive theme – the development of mixed reality by big tech companies, the use of mixed reality for defence, mixed reality as (...) a technology for control of populations in civil society and mixed reality as a technology used in workplace settings. Across these settings, the broad narrative is that mixed reality technologies afford overwhelmingly positive benefits like efficiency and security through their capture, relay and rendition of data – affording a form of anticipatory power to the user. The framing of temporality, we argue, is underlain by social and political values, which represent certain interests, but leave others out in the imagination of mixed reality's technological advance. (shrink)

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only (...) from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics. (shrink)

Scholars are becoming increasingly interested in whether and how government use of Big Data will affect public policy outcomes. Despite such growing scholarly interests, however, little evidence exists on the role Big Data can play in improving government service. We undertake one of the first quantitative studies revealing the potential utility and limitations of “Big Data-based policymaking” by exploring its recent use by the Seoul Metropolitan Government. In 2013, the government introduced the “Owl Bus”—a late-night bus system—the routes of which (...) were selected based on government analyses of Big Data relating to citizens’ late-night taxi and mobile phone use. The findings suggest that the average number of passengers utilizing the Owl Bus routes was significantly greater than that on other daytime bus routes with comparable characteristics. That said, we also present the potential limitations of evidence-based policymaking in general, especially when politics and equity considerations are factored in. (shrink)

In the age of ubiquitous computing and artificially intelligent applications, social machines serves as a powerful framework for understanding and interpreting interactions in socio-algorithmic ecosystems. Although researchers have largely used it to analyze the interactions of individuals and algorithms, limited attempts have been made to investigate the politics in social machines. In this study, I claim that social machines are per se political machines, and introduce a five-point framework for classifying influence processes in socio-algorithmic ecosystems. By drawing from scholars from (...) political theory, I use a notion of influence that functions as a meta-concept for connecting and comparing different conceptions of politics. In this way, I can associate multiple political aspects of social machines from a cybernetic perspective. I show that the framework efficiently categorizes dimensions of influence that shape interactions between individuals and algorithms. These categories are symbolic influence, political conduct, algorithmic influence, design, and regulatory influence. Using case studies, I describe how they interact with each other on online social networks and in algorithmic decision-making systems and illustrate how the framework is able to guide scientists in further research. (shrink)

Large amounts of personal data are generated through young people’s engagements with mobile media, with these data increasingly used by advertisers, content developers and other third parties to profile, predict and position individuals. This has prompted growing concerns over the ability of mobile media users to develop informed stances towards how and why their data is being used, i.e. to build ‘conscious’ and/or ‘resistant’ forms of ‘data agency’. This paper explores ways of developing the critical consciousness and resistant practices of (...) young mobile media users towards personal data. Drawing on research with 27 young people, the paper describes efforts to make representations of third party use of personal data openly available as a basis from which to develop data-savvy tactics and strategies. The results of these interventions – while only partially successful – offer valuable insights into the technical, social and cultural issues that shape young people’s engagement with personal data. The paper concludes by considering how concerns over data agency might be better aligned with the realities of young people’s mobile media use. (shrink)

In this paper, we document the counter-data action and data activism of a grassroots affordable housing advocacy group in Atlanta. Our observation and insight into these data activities and strategies are achieved through ethnographic and engaged research and participatory design. We find that counter-data action through community-collected data is rooted in a legacy of Atlanta’s black activism and black scholarship; that this data activism enabled resource mobilization and critical conscious making; and that design and media production are essential post counter-data (...) action activities in data activism. Based on these findings, we urge the field of open government data to broaden their concept of social impact of data to include the use data to mobilize resources within oppressed communities not to influence policy and government but to build capacities within community in order to transform, not join, political structures. We also advocate that scholars within the fields of open government data, critical data studies, and data activism recognize the legacy and historic practice of data activism by black communities working towards social change. (shrink)

A recent report from the UN makes the case for “global data literacy” in order to realise the opportunities afforded by the “data revolution”. Here and in many other contexts, data literacy is characterised in terms of a combination of numerical, statistical and technical capacities. In this article, we argue for an expansion of the concept to include not just competencies in reading and working with datasets but also the ability to account for, intervene around and participate in the wider (...) socio-technical infrastructures through which data is created, stored and analysed – which we call “data infrastructure literacy”. We illustrate this notion with examples of “inventive data practice” from previous and ongoing research on open data, online platforms, data journalism and data activism. Drawing on these perspectives, we argue that data literacy initiatives might cultivate sensibilities not only for data science but also for data sociology, data politics as well as wider public engagement with digital data infrastructures. The proposed notion of data infrastructure literacy is intended to make space for collective inquiry, experimentation, imagination and intervention around data in educational programmes and beyond, including how data infrastructures can be challenged, contested, reshaped and repurposed to align with interests and publics other than those originally intended. (shrink)

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: natural big data, artificial (...) big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere. (shrink)

The article develops a methodological and empirical approach for gauging the ways Big Data can be collected and distributed through mobile apps. This approach focuses on the infrastructural components that condition the disclosure of smartphone users’ data – namely the permissions that apps request and the third-party corporations they work with. We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone (...) users and investigates the consequences of which and how many mobile apps users download on their phones; the second compares different types of apps in order to study the app ecology and the relationships between app and third-party service providers; and the third focuses on a particular app category and discusses the functional as well as the commercial incentives for permissions and third-party collaborations. Thereby, the article advances an interdisciplinary dialogue between critical data studies, political economy and app studies, and pushes an empirical and critical perspective on mobile communication, app ecologies and data economies. (shrink)

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil. We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest (...) data diaries are able to capture the informal dimension of data-intensive organisations; enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data. (shrink)