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This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

Secondary data collection practices are often opaque to platform users and researchers but known to shape individuals' life chances in significant and unequal ways. In this paper, we articulate a clear relationship between invisible, unwanted data collection and its adverse, downstream consequences for marginalized groups by examining instances in the criminal justice field where social media data function as criminal evidence. We show how social media, as a now common form of courtroom evidence, may structurally work against public defense attorneys and defendants with low socioeconomic status (SES). Drawing on casework interviews with public defenders in New York City, we illustrate the mechanisms by which low-SES criminal defendants are at a disadvantage through overbroad search warrants, asymmetrical cooperation, and prejudicial evidence. We discuss the lessons and implications of our case study for platform privacy and governance research and for the courts. [ABSTRACT FROM AUTHOR]

There are currently no concerted attempts to understand the role of whistleblowers in the new social and political environment created by digital ICTs. Digital ICTs drive an accelerating visibility where elites and citizens constantly acquire new tools to track, surveil, and scrutinize each other. Moreover, these technologies make possible a new kind of invisibility. Increasingly complex modes of digital data production and usage generate grey areas that seem to escape legal jurisdiction and democratic oversight. With their privileged access inside these grey areas, conscientious employees-turned-whistleblowers are likely to become key sources for the disclosure of serious wrongdoing in the coming years. The argument is empirically illustrated through three cases that represent different types of grey areas in advanced democracies: big data surveillance (Edward Snowden), tax havens (Antoine Deltour and the Panama and Paradise Papers), and digital political profiling (Christopher Wylie). [ABSTRACT FROM AUTHOR]

Despite extensive studies on ethical data use and algorithms, little is known about the ethical perspectives of tech workers – insiders of a profession that heavily relies on data and algorithms. Nor have there been sufficient cross-national comparisons of their perspectives. Relying on interviews with 98 tech workers in China and the US, this paper is guided by two questions: (1) What are tech workers' perceptions of big data ethics, and (2) what are the cross-national similarities and differences in China and the US? The study found that there are cross-national similarities in tech workers' cautious enthusiasm about the applications of big data in their work, as well as in their complex and ambivalent ethical perspectives on the use of big data in government digital surveillance. The main cross-national differences occur in tech workers' perceptions of whether big data may reinforce social inequalities. US-based tech workers are concerned about the reinforcement of race and gender-based inequalities through the use of big data, whereas their colleagues in China are optimistic that the use of big data may reduce income-based inequalities across geographical regions. The study's findings have implications for how to leverage tech workers' influence and promote the ethical use of data and algorithms. [ABSTRACT FROM AUTHOR]

This paper discusses the relevance of Georg Lukács' 1923 book History and Class Consciousness in the context of digital capitalism. It does so by analysing how Lukács' concepts of the dialectic of subject and object, ideology, reification, reified consciousness matter today in the context of big data and digital capitalism. The essay shows that History and Class Consciousness' critique of reification, ideology, and reified consciousness remains highly topical in the age of digital capitalism and big data. Lukács' analysis allows us to critically analyse how social media, big data, and various other Internet technologies are used as tools of reification. At the same time, Lukács reminds us that only human praxis can establish alternatives. [ABSTRACT FROM AUTHOR]

This paper seeks to illuminate the significance of datafication for anti-poverty programmes, meaning social protection schemes designed specifically for poor people. The conversion of beneficiary populations into machine-readable data enables two core functions of social protection, those of recognising entitled beneficiaries and assigning entitlements connected to each anti-poverty scheme. Drawing on the incorporation of Aadhaar, India's biometric population database, in the national agenda for social protection, we unpack a techno-rational perspective that crafts datafication as a means to enhance the effectiveness of anti-poverty schemes. Nevertheless, narratives collected in the field show multiple forms of data injustice on recipients, underpinned by Aadhaar's functionality for a shift of the social protection agenda from in-kind subsidies to cash transfers. Based on such narratives the paper introduces a politically embedded view of data, framing datafication as a transformative force that contributes to reforming existing anti-poverty schemes. [ABSTRACT FROM AUTHOR]

This paper explores the relationships of subjects in the context of data and data technologies, and advances an original theoretical framework called Informatic Personhood to better conceptual subjects and their relationships. Because of the enormous structural change that data has contributed to, subjects are sometimes distant and backgrounded in studies of data, despite data having significant impacts on their lives. Data-mediated relationships mean an increased scale to a relationship, with individuals able to connect to much broader contexts of data, but also have these structures reach down to their subjective context through data. Informatic Personhood seeks to capture the dynamics of data present in everyday life, addressing this distance and better conceptualising the scale of data-mediated relationships. This framework has two parts. The first – The Informatic Context – explores salient structural developments around data and conceptualises this as being defined by the presence of 'data interfaces' (that connect individuals to digital contexts), 'data circulation' (trends in the movement and storage of data), and 'data abstraction' (data manipulation practices). The second part concerns the Informatic Person, and the embodied, affective, and sensemaking relationships of individuals occurring across and through the Informatic Context. This framework better addresses the scale of data-mediated relationships, and places subjects firmly in the foreground of how data is understood. [ABSTRACT FROM AUTHOR]

This paper considers how personal data protections figure into the design process of wearable technology. The data question is becoming especially important in the face of recent innovations in biotechnology that capitalize on the new fungibility of biology and electronics, in which new biotech wearables capitalize on the ability to analyze and track changes in blood, sweat, and tears. Interviews and participant observation with wearable tech designers, data scientists, fashion tech entrepreneurs, and select experts in cybersecurity and intellectual property law, reveal a range of approaches to data protection in design within the culture where wearables are beginning to merge with biotech. While a few respondents were extremely vigilant about protecting consumer’s privacy, the majority felt that when consumers ‘opt in’ to data sharing, they should be cognizant of the risks. From their perspective, it is not necessarily the producer’s responsibility to protect user's personal data. These attitudes present a problematic logic, which leaves users vulnerable to data exploitation. The paper concludes by arguing that this laissez-faire culture is the environment in which wearable biotech is being developed and will be deployed. This emerging technology raises issues about bodies, data, and ownership in crucial need of analysis and critique to push its move into the mainstream toward more equitable and inclusive ends. [ABSTRACT FROM AUTHOR]

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 of broadcast news, and Zillow, a marketplace for real estate listings. The range of examples demonstrates how the stakes change from one Big Data initiative to the next. The paper concludes with a set of speculative guidelines for attending to the local conditions in Big Data: get dirty, take a comparative approach, show context, use data to connect people, and create opportunities for the collection of counter-data. When working with Big Data, I argue that thinking locally is thinking critically. [ABSTRACT FROM PUBLISHER]

This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as ‘Big Data’ can be understood as a mode of ‘design-based’ regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the ‘choice architect’. By relying upon the use of ‘nudge’ – a particular form of choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a ‘soft’ form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein [(2008).Nudge. London: Penguin Books] such as placing the salad in front of the lasagne to encourage healthy eating, Big Data analytic nudges are extremely powerful and potent due to their networked, continuously updated, dynamic and pervasive nature (hence ‘hypernudge’). I adopt a liberal, rights-based critique of these techniques, contrasting liberal theoretical accounts with selective insights from science and technology studies (STS) and surveillance studies on the other. I argue that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints. [ABSTRACT FROM PUBLISHER]

It is the purpose of this paper to make explicit the methodology (the theory of the methods) by which we conducted research for an Economic and Social Research Council-funded research project on the relationship of values to value. Specifically, we wanted to study the imperative of Facebook to monetize social relationships, what happens when one of our significant forms of communication is driven by the search for profit, by the logic of capital. We therefore wanted to ‘get inside’ and understand what capital's new lines of flight, informationally driven models of economic expansion, do to social relations. Taking up the challenge to develop methods appropriate to the challenges of ‘big data', we applied four different methods to investigate the interface that is Facebook: we designed custom software tools, generated an online survey, developed data visualizations, and conducted interviews with participants to discuss their understandings of our analysis. We used Lefebvre's [(2004).Rhythmnanalysis: Space, time and everyday life. London: Continuum] rhythmanalysis and Kember and Zylinska's [(2012).Life after new media: Mediation as a vital process.Cambridge, MA: MIT Press] ideas about ‘lifeness’ to inform our methodology. This paper reports on a research process that was not entirely straightforward. We were thwarted in a variety of ways, especially by challenge to use software to study software and had to develop our project in unanticipated directions, but we also found much more than we initially imagined possible. As so few academic researchers are able to study Facebook through its own tools (as Tufekci [(2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In ICWSM ‘14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (pp. 505–514)] notes how, unsurprisingly, at the 2013 ICWSM only about 5% of papers were about Facebook and nearly all of these were co-authored with Facebook data scientists), we hope that our methodology is useful for other researchers seeking to develop less conventional research on Facebook. [ABSTRACT FROM AUTHOR]

The role of data within international development is rapidly expanding. However, the recency of this phenomenon means analysis has been lagging; particularly, analysis of broader impacts of real-world initiatives. Addressing this gap through a focus on data's increasing presence in urban development, this paper makes two contributions. First – drawing from the emerging literature on 'data justice' – it presents an explicit, systematic and comprehensive new framework that can be used for analysis of datafication. Second, it applies the framework to four mapping initiatives in cities of the global South. These initiatives capture and visualise new data about marginalised communities: residents living in slums and other informal settlements about whom data has traditionally been lacking. Analysing across procedural, rights, instrumental and structural dimensions, it finds these initiatives deliver real incremental gains for their target communities. But it is external actors and wealthier communities that gain more; thus, increasing relative inequality. [ABSTRACT FROM AUTHOR]

Environmental data justice (EDJ) emerges from conversations between data justice and environmental justice while identifying the limits and tensions of these lenses. Through a reflexive process of querying our entanglement in non-innocent relations, this paper develops and engages EDJ by examining how it informs the work of the Environmental Data & Governance Initiative (EDGI), a distributed, consensus-based organization that formed in response to the 2016 US presidential election. Through grassroots archiving of data sets, monitoring federal environmental and energy agency websites, and writing rapid-response reports about how federal agencies are being undermined, EDGI mobilizes EDJ to challenge the 'extractive logic' of current federal environmental policy and data infrastructures. 'Extractive logic' disconnects data from provenance, privileges the matrix of domination, and whitewashes data to generate uncertainty. We use the dynamic EDJ framework to reflect on EDGI's public comment advising against the US Environmental Protection Agency's proposed rule for Transparent Science. Through EDJ, EDGI aspires to create new environmental data infrastructures and practices that are participatory and embody equitable, transparent data care. [ABSTRACT FROM AUTHOR]

The concept of witnessing has been used to explore the construction of evidence and experience in settings of law, religion, atrocity, media, history and science. Recent research has examined how digital technologies may multiply the involvement of remote, non-present and unanticipated actors in the witnessing of events. This paper examines what digital data practices at Amnesty International's Decoders initiative can add to the understanding of witnessing. It introduces the notion of 'data witnessing' with reference to four projects on (i) witnessing historical abuses with structured data from digitised documents; (ii) witnessing the destruction of villages with satellite imagery and machine learning; (iii) witnessing environmental injustice with company reports and photographs; and (iv) witnessing online abuse through the classification of Twitter data. These projects illustrate the configuration of experimental apparatuses for witnessing injustices with data. In contrast to accounts which emphasise the presence of an individual human witness at the scene, Amnesty's data practices are conspicuously collective and distributed, rendering the systemic scale of injustices at a distance, across space and time. Such practices may contribute to research on both (new) media witnessing and data politics, suggesting ways in which care, concern and solidarity may be constructed, structured, extended and delimited by means of digital data. [ABSTRACT FROM AUTHOR]

Problems of bias and fairness are central to data justice, as they speak directly to the threat that 'big data' and algorithmic decision-making may worsen already existing injustices. In the United States, grappling with these problems has found clearest expression through liberal discourses of rights, due process, and antidiscrimination. Work in this area, however, has tended to overlook certain established limits of antidiscrimination discourses for bringing about the change demanded by social justice. In this paper, I engage three of these limits: 1) an overemphasis on discrete 'bad actors', 2) single-axis thinking that centers disadvantage, and 3) an inordinate focus on a limited set of goods. I show that, in mirroring some of antidiscrimination discourse's most problematic tendencies, efforts to achieve fairness and combat algorithmic discrimination fail to address the very hierarchical logic that produces advantaged and disadvantaged subjects in the first place. Finally, I conclude by sketching three paths for future work to better account for the structural conditions against which we come to understand problems of data and unjust discrimination in the first place. [ABSTRACT FROM AUTHOR]

This paper examines research about Wikipedia that has been undertaken using big data approaches. The aim is to gauge the coherence as against the disparateness of studies from different disciplines, how these studies relate to each other, and to research about Wikipedia and new social media in general. The paper is partly based on interviews with big data researchers, and discusses a number of themes and implications of Wikipedia research, including about the workings of online collaboration, the way that contributions mirror (or not) aspects of real-world geographies, and how contributions can be used to predict offline social and economic trends. Among the findings is that in some areas of research, studies build on and extend each other's results. However, most of the studies stay within disciplinary silos and could be better integrated with other research on Wikipedia and with research about new media. Wikipedia is among few sources in big data research where the data are openly available, unlike many studies where data are proprietary. Thus, it has lent itself to a burgeoning and promising body of research. The paper concludes that in order to fulfil this promise, this research must pay more attention to theories and research from other disciplines, and also go beyond questions based narrowly on the availability of data and towards a more powerful analytical grasp of the phenomenon being investigated. [ABSTRACT FROM AUTHOR]

As the data phenomenon has grown, researchers have increasingly recognized the need to investigate algorithmic and data-driven technology, practices, and culture. While a number of studies have investigated the process of designing algorithms or examined the contours and consequences of algorithms themselves, more research is needed that details the ways in which users of data-driven technology make sense of algorithmic output and the conditions under which these processes unfold. This paper explores how users derive knowledge from predictive algorithms in medical contexts. Drawing upon interviews and observations of a neonatal intensive care unit, I examine how clinicians use data-driven predictive algorithms designed to forecast the onset of infection. Although the developers intend the technology to function as an early warning system, clinicians do not formulate knowledge from the technology solely as intended. Instead, I find that clinicians engage in a set of interpretive processes that I call ‘conditioned reading,’ and ‘accumulative reading.’ I suggest that these processes are possible due to the particular conditions of the medical context, including the institutional entrenchment of evidence-based medicine. I conclude by arguing that fully theorizing the social implications of data analytics will require researchers to investigate the role of institutional contexts. [ABSTRACT FROM PUBLISHER]

The Facebook experiment of 2014 manipulated the contents of nearly 700,000 users’ News Feeds to induce changes in their emotions. This experiment was widely criticized on ethical grounds regarding informed consent. This controversy, however, diverted attention from a more important concern the experiment was intended to address, which is the impact of Facebook use on well-being. In this paper, I explore the well-being concerns raised by prior research and argue that the experiment does not alleviate them, owing to poor research design. As the question of Facebook's impact on well-being is of great importance, both to Facebook and to society overall, there is a pressing need for more experimental research that is both sensitive to informed consent and carefully designed to yield reliable results. In turn, the lessons of this case have implications for general issues of validity that emerge in Big Data research, now in vogue at major scientific venues. [ABSTRACT FROM PUBLISHER]

Online social networks are an important setting for understanding the intersection of online communities and offline political processes. This paper analyzes the different ways that people discuss elections on Twitter. Using data from a random sample totaling 113,985 tweets and 30,995 users, we examine the differences between users who employ various strategies to talk about US 2010 Congressional candidates. We show that users who simply include the text of a candidate's name in a message exhibit different behaviors than those who use platform-specific mechanisms. Users who employ free-text tend to have younger accounts, are less likely to be ‘verified’, and generate fewer messages about candidates. Furthermore, candidates’ share of the free-text Twitter public has a larger correlation with their vote tallies than @mentions or hashtags. This research has methodological implications for studying the dynamics between online discourse and offline behavior. Overall, our findings support the view that forms of communication that are more accessible are more indicative of broader social trends. [ABSTRACT FROM AUTHOR]