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The increasing use of mobile applications (apps) has turned data harvesting, mining, and distribution into commercial as well as functional backbones of mobile communication. Acknowledging that contemporary markets for mobile communication are increasingly datafied, this article maps and discusses how infrastructures for mobile datafication are controlled. It combines perspectives from critical data studies, political economy of communication, and app studies in an empirical analysis of the 500 most downloaded apps in the Google Play store (November 2020). Focusing on control over and ownership in the three interdependent markets for apps, data accesses, and third-party services, the analysis documents, confirms, and explains established power structures and sheds light on the mobile datafication processes that frame the use of apps and commodification of users. As such, it provides an empirical baseline for future monitoring, and ultimately regulation, of mobile app ecosystems.

This paper considers the subjectivation of students in light of the increasing amounts of digital data that are now being produced within schools. Taking a lead from critical data studies and the s...

In New York City, data documenting complaints made to 311 – the city’s municipal ‘customer service’ agency – are increasingly being analyzed by city agencies, politicians, activists, and residents ...

As the number of digital technologies expands, entering more domains of everyday life, people’s activities, bodies and preferences are rendered into constantly changing flows of digitised information. The interdisciplinary field of critical data studies has emerged in response. In this article, we outline the design and development of methods employed in our new project ‘Living with Personal Data’ as a move towards expanding the knowledge base and methodological approaches of critical data studies. Our approach takes up more-than-human theoretical perspectives and research-creation methods to elicit the affective and multisensory contexts of people’s feelings, practices and imaginaries concerning their digital data. We describe a set of workshops established to experiment with some new methods we have devised for our project’s fieldwork. The article ends with some reflections on what these theories and methods can offer for a reimagined digital data studies that can acknowledge and surface more-than-human dimensions.

This article develops a sociotechnical conceptualisation of data literacies in relation to citizens’ data practices: highlighting the agentic, contextual, critical, and social aspects of data skill...

Data is everywhere. Increasingly woven into the fabric of everyday life, data is ubiquitous. Intensifying a ‘quantifying of the world’ (Mayer-Schonberger & Cukier, 2013) the gathering and applicati...

Programmatic advertising describes techniques for automating and optimizing transactions in the audience marketplace. Facilitating real-time bidding for audience impressions and personalized targeting, programmatic technologies are at the leading edge of digital, data-driven advertising. But almost no research considers programmatic advertising within a general history of information technology in commercial media industries. The computerization of advertising and media buying remains curiously unexamined. Using archival sources, this study situates programmatic advertising within a longer trajectory, focusing on the incorporation of electronic data processing into the spot television business, starting in the 1950s. The article makes three contributions: it illustrates that (1) demands for information, data processing, and rapid communications have long been central to advertising and media buying; (2) automation “ad tech” developed gradually through efforts to coordinate and accelerate transactions; and (3) the use of computers to increase efficiency and approach mathematical optimization reformatted calculative resources for media and marketing decisions.

This paper discusses an explorative approach on strengthening critical data literacy using data science methods and a theoretical framing intersecting educational science and media theory. The goal is to path a way from data-driven to data-discursive perspectives of data and datafication in higher education. Therefore, the paper focuses on a case study, a higher education course project in 2019 and 2020 on education and data science, based on problem-based learning. The paper closes with a discussion on the challenges on strengthening data literacy in higher education, offering insights into data practices and the pitfalls of working with and reflecting on digital data.

Applications of artificial intelligence / machine learning (AI/ML) are dynamic and rapidly growing, and although multi-purpose, are particularly consequential in health care. One strategy for anticipating and addressing ethical challenges related to AI/ML for health care is co-design - or involvement of end users in design. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, the unique features of AI/ML introduce challenges to co-design that are often underappreciated. This review summarizes the research literature on involvement in health care and design, and informed by critical data studies, examines the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ML for health. We suggest that AI/ML technologies have amplified existing challenges related to co-design, and created entirely new challenges. We outline five co-design 'myths and misconceptions' related to AI/ML for health that form the basis for future research and practice. We conclude by suggesting that the normative strength of a co-design approach to AI/ML for health can be considered at three levels: technological, health care system, and societal. We also suggest research directions for a 'new era' of co-design capable of addressing these challenges. Link to full text: https://bit.ly/3yZrb3y

The paper explores the potential and limitations of big data for researching racism on social media. Informed by critical data studies and critical race studies, the paper discusses challenges of doing big data research and the problems of the so called ‘white method’. The paper introduces the following three types of approach, each with a different epistemological basis for researching racism in digital context: 1) using big data analytics to point out the dominant power relations and the dynamics of racist discourse, 2) complementing big data with qualitative research and 3) revealing new logics of racism in datafied context. The paper contributes to critical data and critical race studies by enhancing the understanding of the possibilities and limitations of big data research. This study also highlights the importance of contextualisation and mixed methods for achieving a more nuanced comprehension of racism and discrimination on social media and in large datasets. publishedVersion

Data and data visualisations – in the forms of graphs, charts and maps – are becoming an increasingly important feature of social, public and political life. Yet within existing scholarship, the democratic significance of data visualisations has thus far received minimal attention. This article offers a first systematic attempt to make sense of and scrutinise the role of data visualisation in democracy. We apply deliberative systems theory in the analysis of three original case studies to elucidate how data visualisation can integrate into the overall anatomy of democracy, and to normatively assess how data visualisation contributes towards key democratic ideals. Conclusively, we highlight how critical perspectives on power, ideology and epistemology problematise any simplistic account of how data visualisation matters for democracy. publishedVersion

The increase in the price of gold, due to a shift to safe investments during the global economic crisis, has led to a rapid expansion of gold production. Alongside legal gold mines, wildcat gold mining has developed in French Guiana since the early 2000s. This phenomenon, with its social, environmental and economic consequences, is at the heart of the environmental governance of this territory. However, its difficult quantification is the subject of multiple controversies. Environmental governance is increasingly dependent on metrological regimes aimed at quantifying political action in order to objectify it. This article examines the role of metrology in implementing environmental policies in sparsely populated regions via the example of wildcat gold mining in French Guiana. Based on the study of two observatories, one managed by public authorities, the other by an NGO, we deconstruct their maps and counter-maps of wildcat gold mining. To do so, we make a distinction between measurement, commensuration and its diffusion. This focus on “measurement-commensuration-diffusion” allows us to identify three key phases in the production of nature statistics. We argue that the critical analysis of metrological processes through this three-step framework reveals methodological controversies that reflect different and even divergent political visions. The article also shows that metrological systems for environmental protection are the focus of targeted political disputes. It reports on the current disagreements—not only between the State and NGOs but also within the State itself—on the proposed solutions for fighting the impacts of wildcat gold mining in French Guiana and the broader issues of data production in Amazonia.

International audience; The rise of digital footprints has created a number of promises and expectations for the study of territorial dynamics, particularly those of tourist cities. These footprints would make the observation of visitors' spatial practices possible and make up for the lack of information on these practices at an urban scale. Thus, many studies use data from social networks to study the touristic space at different geographical scales. These studies provide several types of visualisations based on this data, thus making it possible to represent and show a supposedly new touristic space-time-from the heat map to the dashboard, the digital footprints are displayed as processed, aggregated, calculated and smoothed. All these transformations-resulting from algorithmic black boxes that do not allow a precise understanding of the methodologies (often complex and approximate)-are often not very transparent. Consequently, the technicality and opacity of this data make necessary the development of critical approaches that allow the deconstruction of these new mapping registers. Based on data collected on a widely used social network, Instagram, we wish to question digital footprints as a potential tool to observe tourist practices, by going back through the data genealogy, from the map to the footprint. Our approach consists of going back to the initial data and their associated metadata, in order to explore two fundamental dimensions, conditions prerequisite for more complex explorations: time and space. Therefore, we collected a corpus of metadata from photographs published on Instagram between 2016 and 2018 in Biarritz, France, which we analyse following these two axes. Through this exploratory study, we will demonstrate that this data, though very rich, presents a certain number of limits, whether in terms of access to the data itself or its spatiotemporal precision.; La prolifération des traces géonumériques fait émerger diverses promesses et attentes pour l’étude des dynamiques territoriales, en particulier celles des villes touristiques. Ces traces permettraient d’observer les pratiques spatiales des visiteurs et pallieraient le manque d’informations sur ces dernières à l’échelle urbaine. Ainsi, de nombreuses études utilisent les données issues de réseaux sociaux pour étudier l’espace touristique selon différentes mailles géographiques. Ces études proposent plusieurs types de visualisations à partir de ces données, permettant ainsi de représenter et donner à voir un espace-temps touristique prétendument inédit : de la carte de chaleur au tableau de bord, les traces sont présentées traitées, agrégées, calculées, lissées. Toutes ces transformations sont souvent peu transparentes, résultant de boites noires algorithmiques qui ne permettent pas de comprendre précisément les méthodologies - souvent complexes, parfois approximatives - qui sont employées. Dès lors, la technicité et l’opacité de ces données rendent nécessaires le développement d’approches critiques qui permettent de déconstruire ces nouveaux registres de fabrique cartographique. A partir de données récoltées sur un réseau social très utilisé, Instagram, nous souhaitons questionner les traces géonumériques comme outil potentiel d’observation des pratiques touristiques, en remontant dans la généalogie des données : de la carte à la trace. Notre démarche consiste alors à revenir aux données initiales, les semis de points et les métadonnées associées, afin d’explorer trois dimensions fondamentales, conditions pré-requises à des explorations plus complexes : le temps, le lieu et l’individu. Pour cela, nous avons récolté un corpus de métadonnées de photographies publiées sur le réseau social Instagram entre 2016 et 2019 à Biarritz, que nous analysons à travers ces trois axes. A travers cette étude exploratoire, nous démontrons que ces données très riches présentent néanmoins un certain nombre de limites que ce soit dans l’accès aux données elles-mêmes, que dans leur précision spatio-temporelle ou dans la possibilité effective de caractériser les usagers.

Participation in democracy, in today’s digital and datafied society, requires the development of a series of transversal skills, which should be fostered in higher education (HE) through critically oriented pedagogies that interweave technical data skills and practices together with information and media literacies. If students are to navigate the turbulent waters of data and algorithms, then data literacies must be featured in academic development programmes, thereby enabling HE to lead in the development of approaches to understanding and analysing data, in order to foster reflection on how data are constructed and operationalised across societies, and provide opportunities to learn from the analysis of data from a range of sources. The key strategy proposed is to adopt the use of open data as open educational resources in the context of problem and research-based learning activities. This paper introduces a conceptual analysis including an integrative overview of relevant literature, to provide a landscape perspective to support the development of academic training and curriculum design programmes in HE to contribute to civic participation and to the promotion of social justice.

This paper argues that data literacy today needs to go beyond the mere skills to use data. Instead, it suggests the concept of an extended critical big data literacy that places awareness and critical reflection of big data systems at its centre. The presented research findings give first insights into a wide variety of examples of online resources that foster such literacy. A qualitative multi-methods study with three points in time further investigated the views of citizens about the effects of these tools. Key findings are a positive effect particularly of interactive and accessible data literacy tools with appealing visualisations and constructive advice as well as highly insightful suggestions for future tools.

This paper offers a sketch of the complicated conflicts which arise—and metastasize seemingly daily—in the era of Big Data. Given the public’s ubiquitous-yet-ostensibly-voluntary data surrender, and industry’s ubiquitous-yet-ostensibly-anodyne collection of the same, inaction is not an option for any near-just society. By revisiting the philosophical basis for Panoptic apparatus (via Bentham and Foucault), sketching the tumultuous history of US contract law trying to protect the public from itself (from Lochner to Carpenter), and comparing existing industry codes for similarly-situated—read: terrifyingly invasive—fields (e.g., physicians, therapists, attorneys, accountants), the paper will provide a preliminary framework for identifying and confronting the galaxy of problems associated with data analytics.

There have been multiple calls for integrating topics related to fair- ness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collabora- tive auto-ethnography (CAE) engaged in by a MSc Data Science teaching team based at University of Sheffield (UK) Information School where FATE/Critical Data Studies (CDS) topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context of Higher Edu- cation in the UK. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community

This article investigates social inclusion from the perspective of smart mobility and transport, which are core aspects of smart city policy. More specifically, it discusses older people’s mobility practice in smart city environments as a phenomenon at the intersection of age, digital ICT and data. Drawing on mobility studies, communications and critical data studies the article uses the following questions to frame its analysis of inclusive smart transport services from the perspective of old and advanced age: How transport (and) mobility practices interact with mobile ICT use in smart cities? What do we know about the transport mobilities of older people? What do we know about the mobile media and ICT practices of older people? After introducing the concepts of smart city and smart mobility the article discusses these questions through literature review, secondary data, and examples from public transportation services in the city of London, one of Europe’s principal ‘smart’ cities. The analysis highlights age-bias in inherited transport system, gaps in available data about older people’s mobility practices and their media and ICT use, and opportunities for more inclusive (and sustainable) smart transport.

Theoretical debates, research approaches and discursive regimes pertaining to the datafication of everyday life, data and living environments, and the quantified control of the future. Emphasis on the production of databased knowledge and the influence data have on the material and social world. Course presentations include: Week 1 - Introduction – Data Stories and Conceptualizing data? Week 2 - Facts & Data Week 3 - Indicators & Categorization Week 4 - Mapping Week 5 - Administrative and Survey Data Week 6 - Standards Week 7 - Big Data Week 8 - Probability and Risk Week 9 - Data Infrastructure Week 10 - Data Day 4.0 Week 11 - Assemblages, Genealogies and Dynamic Nominalism

How big data and small data shape society. Databases as a form of media. Topics may include: data policy and regulation, the politics and ethics of big data, data and decision-making, and data as discourse. Course presentations include: Week 1 - Introduction - What Are Data? Week 2 - Crowdsourcing & Digital Humanitarianism Week 3 - Open Data Week 4 - Moving, Locating and Sensing You Week 5 - Counting You Week 6 - Social Media You Week 7 - Sorting You Week 8 - Identifying You Week 9 - Watching You Week 10 - Big Data You Week 11 - Data Brokers and You Week 12 - Remembering You Week 13 - Critical Data Studies and Review

Theoretical perspectives, ethical problems, and contemporary issues relevant to communication and data studies. Students will critically examine the rise of ‘big data’ and ‘datafication’ as socio-technical phenomena that have become a crucial part of our communication landscape. Course presentations include: Week 1 – Introduction Week 2 - Conceptualizing data? Week 3 - Indicators, Control Rooms and Dashboards Week 4 - Open Data Week 5 - The Characteristics of Big Data Week 6 - Enablers and Rationale for Big Data Week 7 - Open Government Week 8 - Data Science, Analytics and Smart Cities Week 9 - Data Politics, Activism and Cultures Week 10 - Data Brokers Week 11 - Ethics and the Environment Week 12 - Assemblage, Methods and Review

How do metaphor-driven representations of data mining in corporate media discourse affect understandings of data and data practices? This is, in short, the focal question of this article. Current media discourses of "Big Data" are characterized by a high degree of metaphorization and these metaphors play a key role in how we (mis)understand this ill-defined phenomenon. For example, media scholars Cornelius Puschman and Jean Burgess argue that associated metaphors of big data as a force of nature to be controlled and a form of nourishment/fuel to be consumed lead to a deceptive understanding of big data in terms of natural phenomena. These metaphors are misleading, they argue, as by evoking the image of big data as a force of nature their value is presented as a naturally given, while the fact that value and meaning are actually inscribed by analysis is obscured. This article aims to further deconstruct the representational politics of big data metaphors, directing specific attention to changes in the discursive—specifically metaphorical—framing of data practices in the 1990s, comparing academic discourses with corporate media discourses.

Geographers have been at the forefront of interrogating the changes made possible by the ubiquity of computing and the phenomenon of ‘big data’ in an emerging field known as ‘critical data studies’. In this article, I argue that engagement with the proliferation of computing infrastructures that make these new developments possible in the first place allows critical data studies to gain important historical-geographical perspective, connect to new manifestations of uneven development, and better grasp the role of non-human actors within emerging socio-technical relations. This expanded empirical framing opens up new theoretical implications and opportunities for public engagement with critical infrastructure.

If we are to think critically about Big Data initiatives, we must learn to take them apart. This paper explains how to interrogate Big Data, not as large homogenous resources, but as heterogeneous collections with varied and discordant local ties. My argument focuses on the Big Data of media collections: composite digital repositories of texts, images, and video created in different contexts, but brought together online. The primary example used in this paper is the Digital Public Library of America (DPLA), a collection composed of digitized library, museum and archive records from institutions across the United States. I demonstrate how local readings of DPLA data can uncover schemata, errors, infrastructures, classifications, absences, and rituals that have important origins. Moreover, I explain how identifying these local features can support new forms of scholarship, pedagogy, and advocacy in the face of Big Data. For this last point, I use two additional cases: NewsScape, a real-time archive ...

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.

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

Meta/data are not neutral terms and mean various things for different communities and demographics. One such community that lacks much discussion about their ontologies of meta/data are the LGBTQ communities. With current practices of meta/data production, are the opinions of LGBTQ people properly represented? In this paper we position an ongoing project that is trying to understand the opinions of LGBTQ people regarding meta/data. We briefly provide some context to the broader project and also share our study design for the project. From this paper we hope to position how and why this type of work is necessary for LGBTQ people.

I formulate this chapter as a response to an overarching question that this work, and the network it emerged from, has consistently come across. We have often been asked, that, given that there are myriad ways in which agentic audiences engage, respond, interact with and make sense of media technologies, what can audiences can do to resist intrusive interfaces? I select this question, out of all the questions we have been asked, because the question has beckoned us to think carefully about the variety of implications we see emerging out of this project, and why, in the end, we have struggled with the way the question is phrased. In responding, I make use of the implications arising out of CEDAR’s work to expand the focus from what audiences should do, to what a variety of others need to do, in order to champion the interests of audiences.

The burgeoning field of learning analytics (LA) is gaining significant traction in education, bolstered by the increasing amounts of student data generated through educational software. However, critical discussions of LA are in short supply. Drawing on work in the cultural studies of data and critical algorithm studies, this paper begins by examining three central issues: the distancing and “black boxing” of LA disciplinary practices, the mythologizing of objective data, and the concern for future prediction. The second section describes the design and implementation of the “Learning Analytics Report Card” (LARC), a pilot project that sought to develop experimental approaches to LA. As such, rather than seeking to simply produce analytics, the LARC attempted to foster critical awareness of computational data analysis among teachers and learners.

Scholars and practitioners across domains are increasingly concerned with algorithmic transparency and opacity, interrogating the values and assumptions embedded in automated, black-boxed systems, particularly in user-generated content platforms. I report from an ethnography of infrastructure in Wikipedia to discuss an often understudied aspect of this topic: the local, contextual, learned expertise involved in participating in a highly automated social-technical environment. Today, the organizational culture of Wikipedia is deeply intertwined with various data-driven algorithmic systems, which Wikipedians rely on to help manage and govern the "anyone can edit" encyclopedia at a massive scale. These bots, scripts, tools, plugins, and dashboards make Wikipedia more efficient for those who know how to work with them, but like all organizational culture, newcomers must learn them if they want to fully participate. I illustrate how cultural and organizational expertise is enacted around algorithmic agents by discussing two autoethnographic vignettes, which relate my personal experience as a veteran in Wikipedia. I present thick descriptions of how governance and gatekeeping practices are articulated through and in alignment with these automated infrastructures. Over the past 15 years, Wikipedian veterans and administrators have made specific decisions to support administrative and editorial workflows with automation in particular ways and not others. I use these cases of Wikipedia's bot-supported bureaucracy to discuss several issues in the fields of critical algorithms studies, critical data studies, and fairness, accountability, and transparency in machine learning -- most principally arguing that scholarship and practice must go beyond trying to "open up the black box" of such systems and also examine sociocultural processes like newcomer socialization., 14 pages, typo fixed in v2

Social media platforms and mobile phone data are commonly mined to produce accounts of how people are responding in the aftermath of crisis events. Yet social and mobile datasets have limitations that, if not sufficiently understood and accounted for, can produce specific kinds of analytical and ethical oversights. In this paper, we analyze some of the problems that emerge from the reliance on particular forms of crisis data, and we suggest ways forward through a deeper engagement with ethical frameworks and a more critical questioning of what crisis data actually represents. In particular, the use of Twitter data and crowdsourced text messages during crisis events such as Hurricane Sandy and the Haiti Earthquake raised questions about the ways in which crisis data act as a system of knowledge. We analyze these events from ontological, epistemological, and ethical perspectives and assess the challenges of data collection, analysis and deployment. While privacy concerns are often dismissed when data is scraped from public-facing platforms such as Twitter, we suggest that the kinds of personal information shared during a crisis—often as a way to find assistance and support—present ongoing risks. We argue for a deeper integration of critical data studies into crisis research, and for researchers to acknowledge their role in shaping norms of privacy and consent in data use.

Open government data answers cities' need for new capital resources in a post-recession context and addresses a self-conscious concern about the lagging technological modernization of public institutions. This understanding promotes data-centric management strategies and encourages a mechanistic understanding of how to solve city problems; it leads to new data hybrids born out of data-sharing partnerships between government and the private sector. This proceeding focuses specifically on how, through open data work, staff view their records as administrative and commercial assets and fonts of innovation that improve private and public sector services. Some employees see city records as a source of easy capital, whether cost-saving efficiencies internally or innovation by the private sector. Others see open data as a solution for managing outsized, complex city problems.

In recent years, much has been written on ‘big data’ in both the popular and academic press. After the hubristic declaration of the “end of theory” more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources, and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion or more, are linked together algorithmically that ‘big data’ emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist “accumulation by dispossession” that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of ‘big data’ within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amidst private corporate and academic excitement over new forms of data analysis and visualization, situating big data as a form of capitalist expropriation and dispossession stresses the urgent need for critical, theoretical understandings of data and society., https://ssrn.com/abstract=2709498

In this paper, we discuss the development and piloting of a new methodology for illuminating the socio-material constitution of data objects and flows as data move between different sites of practice. The data journeys approach contributes to the development of critical, qualitative methodologies that can address the geographic and temporal scale of emerging knowledge infrastructures, and capture the ‘life of data’ from their initial generation through to re-use in different contexts. We discuss the theoretical development of the data journeys methodology and the application of the approach on a project examining meteorological data on their journey from initial production through to being re-used in climate science and financial markets. We then discuss three key conceptual findings from this project about: (1) the socio-material constitution of digital data objects, (2) ‘friction’ in the movement of data through space and time and (3) the mutability of digital data as a material property that contributes to driving the movement of data between different sites of practice.

The recently rising field of Critical Data Studies is still facing fundamental questions. Among these is the enigma of digital subjectivation. Who are the subjects of Big Data? A field where this question is particularly pressing is finance. Since the 1990s traders have been steadily integrated into computerized data assemblages, which calls for an ontology that eliminates the distinction between human sovereign subjects and non-human instrumental objects. The latter subjectivize traders in pre-conscious ways, because human consciousness runs too slow to follow the volatility of the market. In response to this conundrum Social Studies of Finance has drawn on Actor-Network Theory to interpret financial markets as technically constructed networks of human and non-human actors. I argue that in order to develop an explicitly critical data study it might be advantageous to refer to Maurizio Lazzarato’s theory of machinic subjugation instead. Although both accounts describe financial digital subjectivation similarly, Lazzarato has the advantage of coupling his description to a clear critique of and resistance to finance.

Questions of data and algorithmic analysis saturated the conference at which Dalton and Thatcher first called for a Critical Data Studies (Dalton and Thatcher, 2014a). Soon thereafter, Taylor published a response to their original call (Dalton and Thatcher, 2014b) that focused on the spatial nature of data and the need to critically contextualize it (Taylor, 2015a). This was one of multiple beginnings of Critical Data Studies, as it will always be three words cobbled together imperfectly signifying diverse sets of work around data’s recursive relationship to society (c.f. Graham 2014; Kitchin 2014, and elsewhere). We highlight that moment not to claim the concept, but to contextualize the focus of this co-interview. When Taylor published their response to Dalton and Thatcher, she focused on the specifically spatial nature of much data, not only in the content of said data, but also in the need to contextualize from where it originates and is put to use. Data varies across space in a variety of ways: from those included or excluded to those who access said data for analysis, manipulation and (re)presentation.Critical Data Studies calls attention to subject formation within these data regimes, for a critical examination of where the interpellation of the individual emerges in algorithmic culture (Striphas, 2015) and, through that, where the cracks and seams, the spaces for resistance and alternatives, might be found. When you append “critical” to a field of study, you run the risk of both offending other researchers, who rightly point out that all research is broadly critical and of bifurcating those who use critical theory from those who engage in rigorous empirical research. Kate Crawford recently observed that the meaning of Critical Data Studies is as political as the data it engages (Crawford, 2015). To extend that, it must remain contestable in order to contest the creation, commodification, analysis, and application of data. Ultimately, Critical Data Studies must make space for the recursive dialogue between the deeply theoretical and the robustly empiric and, in so doing, avoid the hubris of pseudopositivism and technological determinism, in favor of the nuanced and contingent.This is a dialogue between Dalton, Thatcher, and Taylor on the spatial nature of data with respect to Critical Data Studies. It highlights the historical variability of the processes of data production and accumulation and how this, in turn, has resulted in the uneven development of data. We ask both what is missing and what must be brought to the analysis of data in order to respond to the existence of particular dataspheres governed by the kinds of technology available in different locations. In form, Dalton and Thatcher ask the first question and Taylor responds before asking the second, alternating from there. As such, this dialog has many entry and exit points and is not meant as a definitive statement of what Critical Data Studies is or who can speak for it. Rather, we choose to live in the unresolved tensions between researcher and subject, technology and society, space and time (Haraway, 1991), and we encourage our readers to do the same.

Contributing to a rising number of Critical Data Studies which seek to understand and critically reflect on the increasing datafication and digitalisation of governance, this paper focuses on the field of school monitoring, in particular on digital data infrastructures, flows and practices in state education agencies. Our goal is to examine selected features of the enactment of datafication and, hence, to open up what has widely remained a black box for most education researchers. Our findings are based on interviews conducted in three state education agencies in two different national contexts (the US and Germany), thus addressing the question of how the datafication and digitalisation of school governance has not only manifested within but also across educational contexts and systems. As our findings illustrate, the implementation of data-based school monitoring and leadership in state education agencies appears as a complex entanglement of very different logics, practices and problems, producing both new capabilities and powers. Nonetheless, by identifying different types of ‘doing data discrepancies’ reported by our interviewees, we suggest an analytical heuristic to better understand at least some features of the multifaceted enactment of data-based, increasingly digitalised governance, within and beyond the field of education.

Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to imagine and identify their potential utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are \textquoteleftstaged\textquoteright to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures.

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data.

Critical Data Studies (CDS) 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.

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce.

Social media posts are full of potential for data mining and analysis. Recognizing this potential, platform providers increasingly restrict free access to such data. This shift provides new challenges for social scientists and other non-profit researchers who seek to analyze public posts with a purpose of better understanding human interaction and improving the human condition. This paper seeks to outline some of the recent changes in social media data analysis, with a focus on Twitter, specifically. Using Twitter data from a 24-hour period following The Sisters in Spirit Candlelight Vigil, sponsored by the Native Women’s Association of Canada, this article compares three free-use Twitter application programming interfaces for capturing tweets and enabling analysis. Although recent Twitter data restrictions limit free access to tweets, there are many dynamic options for social scientists to choose from in the capture and analysis of Twitter and other social media platform data. This paper calls for critical social media data analytics combined with traditional, qualitative methods to address the developing ‘data gold rush.’

In this chapter I examine the concepts and uses of data as they are expressed in representations of self-tracking (otherwise known as life logging, the quantified self or personal informatics).Self-tracking is not only a technology of the self, but it is also a data practice. Self-tracking may be further conceptualised as a data practice that produces data assemblages. Most recently and noticeably, detailed quantifiable data has become valorised above other forms of information about one’s life, health and wellbeing. I will discuss the valorisation of quantification as a self-tracking data practice, but I also go on to examine alternative data practices with which some people are experimenting as part of self-tracking strategies.

Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of (new) social inequalities through machine learning-based systems.