I propose an approach that expands philosophical views of scientific change, on the basis of an analysis of contemporary biomedical research and recent developments in the philosophy of scientific change. Focusing on the establishment of the exposome in epidemiology as a case study and the role of data as a context for contrasting views on change, I discuss change at conceptual, methodological, material, and social levels of biomedical epistemology. Available models of change provide key resources to discuss this type of (...) change, but I present the need for an approach that models transfer, alignment, and influence as key processes of change. I develop this as a pragmatic approach to scientific change, where processes might change substantially depending on specific circumstances, thus contributing to and complementing the debate on a crucial epistemological issue. (shrink)

Over the last decade, ‘data scientists’ have burst into society as a novel expert role. They hold increasing responsibility for generating and analysing digitally captured human experiences. The article considers their professionalization not as a functionally necessary development but as the outcome of classification practices and struggles. The rise of data scientists is examined across their discursive classification in the academic and economic fields in both the USA and Germany. Despite notable differences across these fields and nations, the article identifies (...) two common subjectivation patterns. Firstly, data scientists are constructed as hybrids, who combine generally conflictive roles as both generalists and specialists; technicians and communicators; data exploiters and data ethicists. This finding is interpreted as demonstrating a discursive distinction between data scientists and other competing and supposedly more one-dimensional professionals, such as statisticians or computer scientists. Secondly, the article uncovers a discursive construction that interpellates data scientists as discoverers of needs. They are imagined as explorative work subjects who can establish growth for digital capitalism by generating behavioural patterns that allow for personalization, customization and optimization practices. (shrink)

Applications of artificial intelligence/machine learning in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ml for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ml introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and critical digital (...) health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ml for health care. We suggest that AI/ml technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ml for health care and conclude with suggestions for addressing these practical and conceptual challenges. (shrink)

This paper reports on a two-year, field-based study set in a charter management organization, a not-for-profit educational organization that operates 18 public schools exclusively in the Black and Latinx communities of South and East Los Angeles. At CMO-LAX, the nine-member Data Team pursues the organization's avowed mission of making public schools data-driven, primarily through the aggregation, analysis, and visualization of digital data derived from quotidian educational activities. This paper draws on the theory of racialized organizations to characterize aspects of data-driven (...) management of public education as practiced by CMO-LAX. I explore two examples of how CMO-LAX shapes data to support racial projects: the reconstruction of the figure of chronic truants and the incorporation of this figure in a calculative regime of student accomplishment. Organizational uses of data support a strategy I call productive myopia, a way of pursuing racial projects via seemingly independent, objective quantifications. This strategy allows the organization to claim to mitigate racial projects and, simultaneously, to accommodate them. This paper concludes by arguing for approaches to research and practice that center racial projects, particularly when data-intensive tools and platforms are incorporated into the provision of public goods and services such as education. (shrink)

This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify and another one that envisions it as a (...) system full of resources. Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms. (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)

This paper develops a philosophical account of moral disruption. According to Robert Baker, moral disruption is a process in which technological innovations undermine established moral norms without clearly leading to a new set of norms. Here I analyze this process in terms of moral uncertainty, formulating a philosophical account with two variants. On the harm account, such uncertainty is always harmful because it blocks our knowledge of our own and others’ moral obligations. On the qualified harm account, there is no (...) harm in cases where moral uncertainty is related to innovation that is “for the best” in historical perspective or where uncertainty is the expression of a deliberative virtue. The two accounts are compared by applying them to Baker’s historical case of the introduction of mechanical ventilation and organ transplantation technologies, as well as the present-day case of mass data practices in the health domain. (shrink)

This paper develops a philosophical account of moral disruption. According to Robert Baker (2013), moral disruption is a process in which technological innovations undermine established moral norms without clearly leading to a new set of norms. Here I analyze this process in terms of moral uncertainty, formulating a philosophical account with two variants. On the Harm Account, such uncertainty is always harmful because it blocks our knowledge of our own and others’ moral obligations. On the Qualified Harm Account, there is (...) no harm in cases where moral uncertainty is related to innovation that is “for the best” in historical perspective, or where uncertainty is the expression of a deliberative virtue. The two accounts are compared by applying them to Baker’s historical case of the introduction of mechanical ventilation and organ transplantation technologies, as well as the present-day case of mass data practices in the health domain. (shrink)

Research on fairness in machine learning (ML) has been largely focusing on individual and group fairness. With the adoption of ML-based technologies as assistive technology in complex societal transformations or crisis situations on a global scale these existing definitions fail to account for algorithmic fairness transnationally. We propose to complement existing perspectives on algorithmic fairness with a notion of transnational algorithmic fairness and take first steps towards an analytical framework. We exemplify the relevance of a transnational fairness assessment in a (...) case study on a disaster response system using images from online social media. In the presented case, ML systems are used as a support tool in categorizing and classifying images from social media after a disaster event as an almost instantly available source of information for coordinating disaster response. We present an empirical analysis assessing the transnational fairness of the application’s outputs-based on national socio-demographic development indicators as potentially discriminatory attributes. In doing so, the paper combines interdisciplinary perspectives from data analytics, ML, digital media studies and media sociology in order to address fairness beyond the technical system. The case study investigated reflects an embedded perspective of peoples’ everyday media use and social media platforms as the producers of sociality and processing data-with relevance far beyond the case of algorithmic fairness in disaster scenarios. Especially in light of the concentration of artificial intelligence (AI) development in the Global North and a perceived hegemonic constellation, we argue that transnational fairness offers a perspective on global injustices in relation to AI development and application that has the potential to substantiate discussions by identifying gaps in data and technology. These analyses ultimately will enable researchers and policy makers to derive actionable insights that could alleviate existing problems with fair use of AI technology and mitigate risks associated with future developments. (shrink)

In response to calls for greater interdisciplinary involvement from the social sciences and humanities in the development, governance, and study of artificial intelligence systems, this paper presents one sociologist’s view on the problem of algorithmic bias and the reproduction of societal bias. Discussions of bias in AI cover much of the same conceptual terrain that sociologists studying inequality have long understood using more specific terms and theories. Concerns over reproducing societal bias should be informed by an understanding of the ways (...) that inequality is continually reproduced in society—processes that AI systems are either complicit in, or can be designed to disrupt and counter. The contrast presented here is between conservative and radical approaches to AI, with conservatism referring to dominant tendencies that reproduce and strengthen the status quo, while radical approaches work to disrupt systemic forms of inequality. The limitations of a conservative approach to racial bias are discussed through the specific example of biased criminal risk assessments and Indigenous overrepresentation in Canada’s criminal justice system. This illustrates the dangers of treating racial bias as a generalizable problem and equality as a generalizable solution, emphasizing the importance of considering inequality in context. Societal issues can no longer be out of scope for AI and machine learning, given the impact of these systems on human lives. This requires engagement with a growing body of critical AI scholarship that goes beyond biased data to analyze structured ways of perpetuating inequality, opening up the possibility for interdisciplinary engagement and radical alternatives. (shrink)

In this review, I analyse how ‘behaviour-based personalisation’ in insurance – that is, insurers’ increased interest in tracking and manipulating insureds’ behaviour with, for instance, wearable devices – has been approached in recent social scientific literature. In the review, I focus on two streams of literature, critical data studies and the sociology of insurance, discussing the new insurance schemes that utilise sensor-generated and digital data. The aim of this review is to compare these two approaches and to analyse what kinds (...) of understandings, methodologies and theoretical perspectives they apply to so-called ‘behaviour-based insurance’. The critical data studies literature emphasises the exploitative aspects of these new technologies and mobilises behaviour-based insurance to exemplify the negative outcomes of digital health. Scholars from the field of the sociology of insurance empirically analyse the practices of behavioural-based personalisation and study how regulating and ‘doing’ insurance affect attempts to personalise it. I highlight the importance of approaching insurance as a specific financial technology and argue that more research is needed to understand the practices of developing behaviour-based insurance schemes and the insureds’ experiences. (shrink)

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, 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. (shrink)

The proliferation of big data analytics in criminal justice suggests that there are positive frames and imaginaries legitimising them and depicting them as the panacea for efficient crime control. Criminological and criminal justice scholarship has paid insufficient attention to these frames and their accompanying narratives. To address the gap created by the lack of theoretical and empirical insight in this area, this article draws on a study that systematically reviewed and compared multidisciplinary academic abstracts on the data-driven tools now shaping (...) decision-making across several justice systems. Using insights distilled from the study, the article proposes three frames for understanding how the technologies are portrayed. Inherent in the frames are a set of narratives emphasising their ostensible status as vital crime control mechanisms. These narratives obfuscate the harms of data-driven technologies and evince idealistic imaginaries of their capabilities. The narratives are bolstered by unequal structural arrangements, specifically the unevenly distributed digital capital with which some are empowered to participate in technology development for criminal justice application and other forms of penal governance. In unravelling these issues, the article advances current understanding of the dynamics that sustain the depiction of data-driven technologies as prime crime prevention and law enforcement tools. (shrink)

Although the rapid growth of digital data and computationally advanced methods in the social sciences has in many ways exacerbated tensions between the so-called ‘quantitative’ and ‘qualitative’ approaches, it has also been provocatively argued that the ubiquity of digital data, particularly online data, finally allows for the reconciliation of these two opposing research traditions. Indeed, a growing number of ‘qualitatively’ inclined researchers are beginning to use computational techniques in more critical, reflexive and hermeneutic ways. However, many of these claims for (...) ‘quali-quantitative’ methods hinge on a single technique: the network graph. Networks are relational, allow for the questioning of rigid categories and zooming from individual cases to patterns at the aggregate. While not refuting the use of networks in these studies, this paper argues that there must be other ways of doing quali-quantitative methods. We first consider a phenomenon which falls between quantitative and qualitative traditions but remains elusive to network graphs: the spread of information on Twitter. Through a case study of debates about nuclear power on Twitter, we develop a novel data visualisation called the modulation sequencer which depicts the spread of URLs over time and retains many of the key features of networks identified above. Finally, we reflect on the role of such tools for the project of quali-quantitative methods. (shrink)

Personal health technologies such as apps and wearables that generate health and behavior data close to the individual patient are envisioned to enable personalized healthcare - and self-care. And yet, they are consumer devices. Proponents of these devices presuppose that measuring will be helpful, and that data will be meaningful. However, a growing body of research suggests that self-tracking data does not necessarily make sense to users. Drawing together data studies and digital health research, we aim to further research on (...) data ambivalence, a term we use to refer to the ambiguities and uncertainties people experience when interpreting their own data, as well as the critical obligation towards cultivating ethically sound uses and responses to such data in context. We develop the relationship between data, interpretation, and context as a central theoretical and practical problem in the datafication of healthcare. We then show how interpretation and context matter for data ambivalence through an empirical study of heart patients with an implanted advanced pacemaker who were offered a Fitbit wristband for self-tracking as part of a research project. We argue that the hope, anxiety, and doubt connected to the promise and accuracy of data are tempered by the context and purpose of self-tracking, and by individual circumstances. Finally, we link the findings on context-sensitivity in data interpretation to questions about response-ability in cloud-based care infrastructures. We discuss the ethical dilemmas associated with the use of commercial wellness-technologies in healthcare, and with researching such emerging practices. (shrink)

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and should relate. Inspired (...) by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given. (shrink)

The nature of farming is – still – an essentially biological, and thus volatile, system, which poses substantial challenges to its integration into financialized capitalism. Financial investors often seek stability and predictability of returns that are hardly compatible with agriculture – but which are increasingly seen as achievable through data and digital farming technologies. This paper investigates how farmland investment brokers engage with, perceive, and produce farming data for their investors within a co-constructive process. Tackling land’s ‘stubborn materiality’ for investment, (...) I argue, has material and immaterial components: it includes the re-imagination of farming as a financial asset that delivers reliable income streams for investors; and the re-engineering of farmland’s concrete materialities with digital farming technologies. Farmland investment brokers develop investor-suitable farmland imaginaries, underpinned by storytelling as well as the calculative ‘evidence’ of (digital) data. At the same time, digital technologies have become a key tool for transforming farms into ‘investment grade assets’ endowed with the rich data on farm performance and financial returns requested by investors. I conclude that the assetization and digitization of farmland need to be seen as closely intertwined and mutually reinforcing processes and identify key areas for future research on this intersection. (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)

The current research examines the emergent literature of Critical Data Studies, and particularly aligns with Michael and Lupton’s (2016) manifesto calling for researchers to study the Public Understanding of Big Data. The aim of this paper is to explore Irish stakeholders’ narratives on data sharing in agriculture, and the ways in which their attitudes towards different data sharing governance models reflect their understandings of data, the impact that data hold in their lives and in the farming sector, as well as (...) their preferences for how data should be governed within agriculture. Seven focus groups were held in 2019 with Irish stakeholders from a variety of backgrounds, including agri-researchers, those working in SMEs, and farmers of varying ages and sectors. The primary activities carried out during these focus groups centred upon asking participants to discuss four different data sharing governance models, and to work their way through a set of value cards relating to these models. Focus group results are studied using an inductive, data-driven form of thematic analysis (Braun and Clarke 2006). Five primary themes cross-cut these focus groups: 1) Desire for a data intermediary, 2) Reversing the value chain, 3) Categorisation of data, 4) The common good, and 5) Potential danger in data sharing. These themes are explored in the paper through a detailed discussion of the focus group results, in which the authors track the manifestation of these themes across focus groups, and the ways they sometimes morphed or changed depending upon the participating stakeholder group. (shrink)

I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is (...) underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk. (shrink)

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. (shrink)

The financial applications of data technology have enabled the rise of Chinese fintech industries. As part of people's everyday lives, fintech apps have helped companies collect vast amounts of user data for business profit and social good. This paper takes an open-systems approach to study the constructs of this emerging idea of data governance, particularly its operational logic, involved stakeholders, and socio-cultural consequences in the context of fintech industries in China. It asserts that data governance at the company level has (...) been realized through three types of tasks: standardization, configuration, and monetization of big data. These tasks are oriented by the imperative of business innovation which is considered pivotal for the company to survive the competition. An ensemble of internal and external actors joined the governing process, but internal actors hold the stake. The goal of innovation drives the companies to speedily collect data from a wider variety of sources, thus having to establish more sophisticated systems to manage and utilize those data. These complicated systems, however, have urged the government to strengthen regulatory controls alongside the general support of business innovation. This study unveils the performative aspects of big-data-based innovation at fintech firms. It also helps to understand the pathology of the innovation-governance paradox shared in the data economy in the global context. (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)

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)

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)

Social media offers an attractive site for Big Data research. Access to big social media data, however, is controlled by companies that privilege corporate, governmental, and private research firms. Additionally, Institutional Review Boards’ regulative practices and slow adaptation to emerging ethical dilemmas in online contexts creates challenges for Big Data researchers. We examine these challenges in the context of a feminist qualitative Big Data analysis of the hashtag event #WhyIStayed. We argue power, context, and subjugated knowledges must each be central (...) considerations in conducting Big Data social media research. In doing so, this paper offers a feminist practice of holistic reflexivity in order to help social media researchers navigate and negotiate this terrain. (shrink)

Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are absent from these debates. This paper addresses this gap, (...) reporting on a survey of the public on their views of data management approaches, undertaken by the authors and administered in the UK, where departure from the EU makes future data legislation uncertain. The survey found that respondents dislike the current approach in which commercial organizations control their personal data and prefer approaches that give them control over their data, that include oversight from regulatory bodies or that enable them to opt out of data gathering. Variations of data trusts – that is, structures that provide independent stewardship of data – were also preferable to the current approach, but not as widely preferred as control, oversight and opt out options. These features therefore constitute ‘good data management’ for survey respondents. These findings align only in part with principles of good data identified by policy experts and researchers. Our findings nuance understandings of good data as a concept and of good data management as a practice and point to where further research and policy action are needed. (shrink)

Predictive uses of data are becoming widespread in institutional settings as actors seek to anticipate people and their activities. Predictive modeling is increasingly the subject of scholarly and public criticism. Less common, however, is scrutiny directed at the data that inform predictive models beyond concerns about homogenous training data or general epistemological critiques of data. In this paper, I draw from a qualitative case study set in higher education in the United States to investigate the making of data. Data analytics (...) projects at universities have become more pervasive and intensive to better understand and anticipate undergraduate student bodies. Drawing from 12 months of ethnographic research at a large public university, I analyze the ways data personnel at the institution—data scientists, administrators, and programmers—sort student data into “attributes” and “behaviors,” where “attributes” are demographic data that students “can’t change.” “Behaviors,” in contrast, are data defined as reflective of what students can choose: attending and paying attention in class, studying on campus, among other data which personnel categorize as what students have control over. This discursive split enables the institution nudge students to make responsible choices according to behavior data that correlate with success in the predictive model. In discussing how personnel type, sort, stabilize, and nudge on behavior data, this paper examines the contingencies of data making processes and implications for the application of student data. (shrink)

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production and circulation is then introduced to (...) problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

In this article, we introduce and demonstrate the concept-metaphor of broken data. In doing so, we advance critical discussions of digital data by accounting for how data might be in processes of decay, making, repair, re-making and growth, which are inextricable from the ongoing forms of creativity that stem from everyday contingencies and improvisatory human activity. We build and demonstrate our argument through three examples drawn from mundane everyday activity: the incompleteness, inaccuracy and dispersed nature of personal self-tracking data; the (...) data cleaning and repair processes of Big Data analysis and how data can turn into noise and vice versa when they are transduced into sound within practices of music production and sound art. This, we argue is a necessary step for considering the meaning and implications of data as it is increasingly mobilised in ways that impact society and our everyday worlds. (shrink)

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 sociology of numbers, the paper draws on staff interviews in three Australian secondary schools to explore the various types of student data being generated, and the forms of student subjectivities that result. In particular, the paper contrasts the ‘holistic’ possibilities that some school leaders and administrators ascribe to data (...) in terms of expanding the capacity to ‘know’ students, against the limited ways that data is actually being used within the schools. Most notably, the paper details how digital data appears to be configured within schools’ official data procedures and practices to build student subjectivities and position students in narrow terms of performance and attendance. The paper also highlights how teachers make practical use of these limited data ‘profiles’ in a relational manner – as a way of stimulating dialogue with students to know them better, rather than a source of precise calculation. In this sense, the paper considers how ‘data’ might be reframed in educational discourse as a practical starting-point for teacher inquiry and professional judgment rather than an imagined source of all-encompassing knowledge. (shrink)

This article introduces the methodology of the ‘Ontology Explorer’, a semantic method and JavaScript-based open-source tool to analyse data models underpinning information systems. The Ontology Explorer has been devised and developed by the authors, who recognized a need to compare data models collected in different formats and used by diverse systems. The Ontology Explorer is distinctive firstly because it supports analyses of information systems that are not immediately comparable and, secondly, because it systematically and quantitatively supports discursive analysis of ‘thin’ (...) data models – also by detecting differences and absences through comparison. When applied to data models underpinning systems for population management, the Ontology Explorer enables the apprehension of how people are ‘inscribed’ in information systems: which assumptions are made about them, and which possibilities are excluded by design. The Ontology Explorer thus constitutes a methodology to capture authorities’ own imaginaries of populations and the ‘scripts’ through which they enact actual people. Furthermore, the method allows the comparison of scripts from diverse authorities. This is exemplified by illustrating its functioning with information systems for population management deployed at the European border. Our approach integrates a number of insights from early infrastructure studies and extends their methods and analytical depth to account for contemporary data infrastructures. By doing so, we hope to trigger a systematic discussion on how to extend those early methodical innovations at the semantic level to contemporary developments in digital methods. (shrink)

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. (shrink)

Undergirding China’s Belt and Road Initiative’s lofty promise of global connectivity are existing connections between the PRC’s implementation of planetary-scale observation systems for environmental sustainability and the recognizably nefarious policies of localized, colonial surveillance of Turkic minorities in the Xinjiang Uyghur Autonomous Region (XUAR). My article examines how the recently alleged genocide in XUAR becomes the afflicted topos where both the rhetoric and practices of monitoring differently complex systems come together. Such complex connections require a recursive analysis, one which further (...) distinguishes between recursion as an actual technique used in remote sensing and algorithmic processes and recursion as a heuristic in discourses about these computational processes and their effects on controlled populations and territories. This article, following recent interest in the intersections of geopolitics, computational design, digital capitalism and colonialism, argues that the Chinese government’s multipronged investments in environmental sciences-related sensing and imaging technologies do not simply help track its own citizens and foreign populations but also how these groups are increasingly monitored by them. I examine how the Digital Belt and Road’s Science Plan frames the environment, including sovereign territories, peoples, and natural resources as data assets. The rest of my discussion turns away from state-sponsored Earth sciences to examine Anglophone media and human rights groups’ use of satellite imageries and databases to evidence the state’s construction of internment facilities and other surveillance mechanisms in XUAR. Alleged algorithms of oppression enclose the XUAR as a black box of the police state, but digital infographic interfaces like the Xinjiang Data Project, which attempts to expose the PRC’s relegation of Uyghurs to biometric and surveillance data, only furthers a recursive datafication of XUAR. (shrink)

Purpose This paper aims to examine how metadata taxonomies in embodied computing databases indicate context and describe ways to track the evolution of the embodied computing industry over time through digital media archiving. Design/methodology/approach The authors compare the metadata taxonomies of two embodied computing databases by providing a narrative of their top-level categories. After identifying these categories, they describe how they structure the databases around specific themes. Findings The growing wearables market often hides complex sociotechnical tradeoffs. Marketing products like Vandrico (...) Inc.’s Wearables Database frame wearables as business solutions without conveying information about the various concessions users make. Potential solutions to this problem include enhancing embodied computing literacy through the construction of databases that track media about embodied computing technologies using customized metadata categories. Databases such as FABRIC contain multimedia related to the emerging embodied computing market – including patents, interviews, promotional videos and news articles – and can be archived through user-curated collections and tagged according to specific themes. One of the benefits of this approach is that users can use the rich metadata fields to search for terms and create curated collections that focus on tradeoffs related to embodied computing technologies. Originality/value This paper describes the importance of metadata for framing the orientation of embodied computing databases and describes one of the first attempts to comprehensively track the evolution of embodied computing technologies, their developers and their diverse applications in various social contexts through media archiving. (shrink)

This article presents our experience as a multidisciplinary team systematizing and analyzing the transcripts from a large-scale (1.775 conversations) series of conversations about Chile’s future. This project called “Tenemos Que Hablar de Chile” [We have to talk about Chile] gathered more than 8000 people from all municipalities, achieving gender, age, and educational parity. In this sense, this article takes an experiential approach to describe how certain interdisciplinary methodological decisions were made. We sought to apply analytical variables derived from social science (...) theories and operationalize them through modern linguistics to guide a more theoretically informed natural language processing. The analysis was divided into three stages: (1) a descriptive analysis adapting descriptions of computational grounded theory, (2) a futurization analysis operationalizing concepts from futures studies, and (3) an argumentative analysis operationalizing concepts from argumentation theory. Overall, our methodological experimentation shed light on potential learnings for integrating a multidisciplinary perspective on NLP analysis with sensitive social content. Firstly, we developed a strategy for translation of knowledge based on the construction of what we called "analytical categories” in which a normative expectation or descriptive dimension was identified in the body of literature, operationalized through linguistics, and programmed in Python or R. Ultimately, we seek to reflect on the importance interdisciplinarity not only as means to find new analysis ideas but rather, to incorporate the critical, political and epistemological points of view to understand analysis as complex socio-technical processes. (shrink)