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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).

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 ...

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.

This paper discusses the relevance of Georg Lukacs’ 1923 book History and Class Consciousness in the context of digital capitalism. It does so by analysing how Lukacs’ concepts of the dialectic of ...

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 subj...

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 DNAr...

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 innovation...

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...