Although the Internet is frequently referred to as a global public resource, its functioning remains predominantly controlled by private actors. The Internet brought about significant shifts in the way we conceptualize governance. In particular, the handling of “big data” by private intermediaries has a direct impact on routine practices and personal lives. The implementation of the “right to be forgotten” following the May 2014 decision of the Court of Justice of the European Union against Google blurs the boundaries between the (...) public and the private, and extends the responsibilities of the latter to court-style decision making. This article analyzes the regulatory developments in this area and the implications of outsourcing of important governance practices to private intermediaries. It looks at the decision-making process for the “right to be forgotten” to illustrate the extent to which the hybridization of such procedures results in new arrangements between public and private ordering in Internet governance. (shrink)

In discussions on the ethics of genome collections and biobanks, the main worry about whether we are permitted to collect and use individuals’ genomic and genetic data is the potential for the violation of individuals’ right to informational privacy. Yet, if we do not permit these endeavors, we risk giving up on the future benefits of biomedical research. In this paper, I describe a private venture in blockchain genomics that seeks to provide an apt solution to concerns about potential privacy (...) violations in genome collections and biobanks. I then provide some reasons to doubt the kind of solution to the dilemma that such start-ups propose. I argue that the sort of autonomy that grounds the value of the right to informational privacy cannot be secured with blockchain technology alone. So, blockchain genomics ventures are insufficient to establish the permissibility of genome collections and biobanks. I then discuss an additional ethical challenge to genomic biobanks. This challenge takes the form of doubting that future benefits that result from biomedical research dependent on genomic databases will outweigh the costs. Despite criticisms of genomic research, I claim that it is reasonable to think that future net benefits will be gained from research on such databases. Therefore, I conclude that there remains a dilemma in the ethics of genomic biobanks. (shrink)

Through the application of machine learning techniques, this paper aims to estimate the importance of messages with ideological load during the elections held in Spain on May 24th, 2015 posted by Twitter’s users, as well as other variables associated with the publication of these types of messages. Our study collected and analysed 24,900 tweets associated to two of the main trending topics’ hashtags used in the election day and build a predictive model to infer the ideological orientation for the messages (...) which made use of these hashtags during Election Day. This approach allows us to classify the ideological orientation of all collected tweets, instead of only tweets that explicitly express their ideological or partisan preferences in the messages. Using the ideological orientation for all tweets predicted by our model, it was possible to identify how messages with a defined ideological load were pushed forward by users with leftist tendencies. We also observed a relationship between these messages and the partisan orientation of those who published them. (shrink)

This article reviews the qualitative changes that big data technology introduced to society, particularly changes that affect how individuals control the access, use and retention of their personal data. In particular interest is whether the practice of privacy self-management in this new context could still ensure the informed consent of individuals to the privacy terms of big data companies. It is concluded that that accepting big data companies’ privacy policies falls short of the disclosure and understanding requirements for informed consent. (...) The article argues that the practice of privacy self-management could remain viable if the review, understanding and acceptance of privacy agreements is streamlined, standardized and automated. Technology should be employed to counter the privacy problems created by big data technology. The creation of the privacy exchange authorities is proposed as a solution to the failures of privacy self-management. The PEA are intermediaries that empower individuals to define their own privacy terms and express informed consent in their dealings with data companies. They will create the technological infrastructure for individuals to select their own privacy terms from a list of standard choices, potentially only once. The PEA will further mediate the delivery and authentication of the individual users’ privacy terms to data companies. A logical proof of concept is offered, illustrating the potential steps involved in the creation of the PEA. (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)

In the past few years, several democratic governments have published their National AI Strategies (NASs). These documents outline how AI technology should be implemented in the public sector and explain the policies that will ensure the ethical use of personal data. In this article, I examine these documents as political texts and reconstruct the political imaginary that underlies them. I argue that these documents intervene in contemporary democratic politics by suggesting that AI can help democracies overcome some of the challenges (...) they are facing. To achieve this, NASs use different kinds of imaginaries—democratic, sociotechnical and data—that help citizens envision how a future AI democracy might look like. As part of this collective effort, a new kind of relationship between citizens and governments is formed. Citizens are seen as autonomous data subjects, but at the same time, they are expected to share their personal data for the common good. As a result, I argue, a new kind of political imaginary is developed in these documents. One that maintains a human-centric approach while championing a vision of collective sovereignty over data. This kind of political imaginary can become useful in understanding the roles of citizens and governments in this technological age. (shrink)

Drawing primarily on ethnographic research performed in a city in Romania, this paper provides a thick description of police practices and information systems in that municipality. It shows various ways in which technologies mediate policing practitioners’ perceptions, decisions and actions. Bringing some additional material from a case in the Dutch police in which they build risk profiles predicated on real-time data from a sensor network, the paper highlights new phenomena with ethical implications emerging at the intersection of information infrastructures and (...) policing practices. In particular, it shows a solidifying effect of technologically mediated suspicion, a sedimentation process in technological infrastructures and the formation of pockets of prejudice in the layers of software code. To adequately account for these phenomena and others, the paper provides a set of arguments and conditions for a sedimentology of infrastructures. At the same time, it offers the first steps in a larger research project that would adapt and test the limits of a geological vocabulary and approach to understand smart urban environments. (shrink)

Digital transformations and processes of “datafication” fundamentally reshape how information is produced, circulated and given meaning. In this article, we provide a concept of “datastructuring” which seeks to capture this reshaping as both a product of and productive of social activity. To do this we focus on how new forms of social action map onto and are enabled by technological changes related to datafication, and how new forms of datafied social action constitute a form of knowledge production which becomes embedded (...) in technologies themselves. We illustrate the potential of the datastructuring concept with empirical examples which also serve to highlight some new avenues for research and some empirical questions to explore further. We suggest a focus on datastructuring can ignite scholarly debates across disciplines that may share an interest in the technological configurations, sorting activities, and other socio-material forces that shape digital spaces, but which are rarely brought together. Such cross-disciplinary conceptualizations may give more attention to how information is structured and organized, becomes “algorithmically recognizable”, and emerges as visible in digital, datafied spaces. Such a concept, we suggest, may help us better understand the novel ways in which “backstage datawork” and “data sorting processes” gain traction in political interventions, commercial processes, and social ordering. (shrink)

Making publics visible through digital traces has recently generated interest by practitioners of public engagement and scholars within the field of digital methods. This paper presents an experiment in moving such methods into critical proximity with political practice and discusses how digital visualizations of topical debates become appropriated by actors and hardwired into existing ecologies of publics and politics. Through an experiment in rendering a specific data-public visible, it shows how the interplay between diverse conceptions of the public as well (...) as the specific platforms and data invoked, resulted in a situated affordance-space that allowed specific renderings take shape, while disadvantaging others. Furthermore, it argues that several accepted tropes in the literatures of digital methods ended up being problematic guidelines in this space. Among these is the prescription to shown heterogeneity by pushing back at established media logics. (shrink)

Implications of algorithmic mediation can be studied through the artefact itself, peoples’ practices, and the social/political/economical arrangements that affect and are affected by such interactions. Most studies in Academic social media (ASM) focus on one of these elements at a time, either examining design elements or the users’ behaviour on and perceptions of such platforms. We take a multi-faceted approach using affordances as a lens to analyze practices and arrangements traversed by algorithmic mediation. Following our earlier studies that examined the (...) artefact, this study’s aim is to understand how algorithmic mediation in ASM may shape researchers’ perceptions. We conducted online in-depth interviews with show and tell (n = 11) with ASM users from four countries, about their use of ASM. Data were analyzed using thematic analysis, and the codes were clustered in themes and validated via peer debriefing process. Four themes—related to exposure to content, visibility, ethical or legal infringements, and engagement—are discussed considering implications of datafication and findability. We conclude that algorithmic mediation constructs a narration of the relevant other in ASM, configuring the other as participatory and productive. (shrink)

Due to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the performance (...) of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets. (shrink)

There are many ways in which biases can enter processes of scientific reasoning. One of these is what Ludwik Fleck has called a “harmony of illusions”. In this paper, Fleck’s ideas on the relevance of social mechanisms in epistemic processes and his detailed description of publication processes in science will be used as a starting point to investigate the connection between cognitive processes, social dynamics, and biases in this context. Despite its usefulness as a first step towards a more detailed (...) analysis, Fleck’s account needs to be updated in order to take the developments of digital communication technologies of the 21st century into account. Taking a closer look at today’s practices of science communication shows that information and communication technologies (ICTs) play a major role here. By presenting a detailed case study concerning the database SCOPUS, the question will be investigated how such ICTs can influence the division of epistemic labour. The result will be that they potentially undermine the epistemic benefits of social dynamics in science communication due to their inherent tendency to reduce the diversity of scientific hypotheses and ideas. (shrink)

In this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can (...) help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances. (shrink)

Purpose This conceptual contribution is based on the observation that digital inequalities literature has not sufficiently considered digital footprints as an important social differentiator. The purpose of the paper is to inspire current digital inequality frameworks to include this new dimension. Design/methodology/approach Literature on digital inequalities is combined with research on privacy, big data and algorithms. The focus on current findings from an interdisciplinary point of view allows for a synthesis of different perspectives and conceptual development of digital footprints as (...) a new dimension of digital inequality. Findings Digital footprints originate from active content creation, passive participation and platform-generated data. The literature review shows how different social groups may experience systematic advantages or disadvantages based on their digital footprints. A special emphasis should be on those at the margins, for example, users of low socioeconomic background. Originality/value By combining largely independent research fields, the contribution opens new avenues for studying digital inequalities, including innovative methodologies to do so. (shrink)

Although the terminology of Big Data has so far gained little traction in economics, the availability of unprecedentedly rich datasets and the need for new approaches – both epistemological and computational – to deal with them is an emerging issue for the discipline. Using interviews conducted with a cross-section of economists, this paper examines perspectives on Big Data across the discipline, the new types of data being used by researchers on economic issues, and the range of responses to this opportunity (...) amongst economists. First, we outline the areas in which it is being used, including the prediction and ‘nowcasting’ of economic trends; mapping and predicting influence in the context of marketing; and acting as a cheaper or more accurate substitute for existing types of data such as censuses or labour market data. We then analyse the broader current and potential contributions of Big Data to economics, such as the ways in which econometric methodology is being used to shed light on questions beyond economics, how Big Data is improving or changing economic models, and the kinds of collaborations arising around Big Data between economists and other disciplines. (shrink)

Can citizens impact the broader discourse about an organization and its legitimacy? While social media have empowered citizens to publicly question firms through large volumes of online evaluations, the high heterogeneity of their evaluations dilutes their impact. Our empirical study applying a threshold vector autoregressive model (TVAR) analysis of 2.5 million tweets and 1,786 news media articles tests the condition by which the heterogeneity of online evaluations converges and influences the broader media discourse. Although social media evaluations do not initially (...) influence media legitimacy, they become influential after reaching a tipping point of refracted attention, which is created by high volume and convergence of individual evaluations around few aggregative frames. Thus, social media storms may influence the broader discourse about an organization when this discourse converges and reaches a tipping point, rather than merely through the massive participation of citizens. (shrink)

Drawing on interviews with people from the advertising and technology industry, legal experts and policy makers, this paper assesses the rise of emotion detection in digital out-of-home advertising, a practice that often involves facial coding of emotional expressions in public spaces. Having briefly outlined how bodies contribute to targeting processes and the optimisation of the ads themselves, it progresses to detail industrial perspectives, intentions and attitudes to data ethics. Although the paper explores possibilities of this sector, it pays careful attention (...) to existing practices that claim not to use personal data. Centrally, it argues that scholars and regulators need to pay attention to the principle of intimacy. This is developed to counter weaknesses in privacy that is typically based on identification. Having defined technologies, use cases, industrial perspectives, legal views and arguments about jurisprudence, the paper discusses this ensemble of perspectives in light of a nationwide survey about how UK citizens feel about the potential for emotion detection in out-of-home advertising. (shrink)

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets (...) drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world. (shrink)

The paper extends previous accounts of informational privacy as a contextual notion. Where previous accounts have focused on interpretations of the privacy context itself as being contextual and open for negotiation and interpretation, this paper extends those analyses and shows that personal information itself is in fact best understood as contextual and situational—and as such open for interpretation. The paper reviews the notion of information as it has been applied in informational privacy and philosophy of information, and suggests that personal (...) information ought to be regarded as communicative acts. The paper suggests a reconceptualization of informational privacy from having its focus on controlling, limiting, and restricting access to material carriers of information to a focus on a regulation of the use, analysis, and interpretation of personal information. (shrink)

This paper presents an analysis of interviews, focus groups and workshops with employees in the technical administration in the municipality of Copenhagen in the year after it won a prestigious Smart City award. The administration is interpreted as a ‘most likely’ to succeed in translating the idealised version of the smart city into a workable bureaucratic practice. Drawing on the work of Orlikowski and Gash, the empirical analysis identifies and describes two incongruent ‘technological frames’ that illustrates different ways of making (...) sense of data and the smart city within this single organisational unit. One is called the experimentalist’s credo and it is characterised by inspiration from the development of an Internet of Things as well as a readiness to learn from the open source community in software development. The other is called the data-owners vocation and it is characterised by a more situated approach that interprets data as strategic and political. It is argued that the existence of these frames provides two insights relevant for the literature on smart cities. First, they illustrate that one should be careful not to reify the smart city as a phenomenon that can be criticised in generic terms. Second, they suggest that even if there exists a transition toward the implementation of a technocratic smart city paradigm across public administrations, this paradigm is not unique in its focus on markets and evidence in governance. (shrink)

Digital social analytics is a subset of Big Data methods that is used to understand the social environment in which people and organizations have to act. This paper presents an analysis of eight projects that are experimenting with the use of these methods for various purposes. It shows that two specific technological features influence the work with such methods in all the cases. The first concerns the need to distribute choices about the structure of data to third-party actors and the (...) second concerns the need to balance machine intelligence and human intuition when automating the analysis. These features set specific conditions for knowledge production, and the paper identifies two opposite approaches for engaging with each of these conditions. These features and approaches are finally combined into a two-dimensional affordance space that illustrates how there is flexibility in the way project leaders interact with the features of the data environment. It thereby also shows how digital social analytics come to have different affordances for different projects. (shrink)

Accounts of big data practices often assume that they target individuals. Personalization, with all the risks of discrimination and bias it entails, has been the critical focus in accounts of consumption, government, social media, and health. This paper argues that personalization through models using large-scale data is part of a more expansive change in probabilization that, in principle, is not reducible to individual or ‘personal’ attributes and actions. It describes the ‘personalization’ of an online grocery shopping recommender system to list (...) a small number of grocery items of personal relevance for each of the millions of online grocery shoppers at a major UK supermarket chain. Drawing on a theory of probability proposed by the philosopher of science Karl Popper and anthropological work on shopping, it suggests that the attempt to generate personalized predictions necessarily incorporates impersonal relations to others and things. Using a mixture of discourse analysis and code-based reconstruction of key elements of the recommender system, it suggests that personalization is one facet of an open-ended weave of propensities associated with people and things in contemporary big data configurations. The paper explores how, in the context of recommender systems, the constitutive incompleteness of shopping lists, their propensity to expand or change, might be more important than their capacity to be personalized. (shrink)

This paper addresses a problem that has so far been neglected by scholars investigating the ethics of Big Data and policy makers: that is the ethical implications of Big Data initiatives’ environmental impact. Building on literature in environmental studies, cultural studies and Science and Technology Studies, the article draws attention to the physical presence of data, the material configuration of digital service, and the space occupied by data. It then explains how this material and situated character of data raises questions (...) concerning the ethics of the increasingly fashionable Big Data discourses. It argues that attention should be paid to the vocabulary currently used when discussing the governance of data initiatives; the internal tension between current data initiatives and environmental policies; issues of fair distribution. The article explains how taking into account these aspects would allow for a more responsible behaviour in the context of data storage and production. (shrink)

This paper addresses a problem that has so far been neglected by scholars investigating the ethics of Big Data and policy makers: that is the ethical implications of Big Data initiatives’ environmental impact. Building on literature in environmental studies, cultural studies and Science and Technology Studies, the article draws attention to the physical presence of data, the material configuration of digital service, and the space occupied by data. It then explains how this material and situated character of data raises questions (...) concerning the ethics of the increasingly fashionable Big Data discourses. It argues that attention should be paid to the vocabulary currently used when discussing the governance of data initiatives; the internal tension between current data initiatives and environmental policies; issues of fair distribution. The article explains how taking into account these aspects would allow for a more responsible behaviour in the context of data storage and production. (shrink)

This paper aims to clarify the consequences of new scientific and philosophical approaches for the practical-theoretical framework of modern developmental biology. I highlight normal development, and the instructive-permissive distinction, as key parts of this framework which shape how variation is conceptualised and managed. Furthermore, I establish the different dimensions of biological variation: the units, temporality and mode of variation. Using the analytical frame established by this, I interpret a selection of examples as challenges to the instructive-permissive distinction. These examples include (...) the phenomena of developmental plasticity and transdifferentiation, the role of the microbiome in development, and new methodological approaches to standardisation and the assessment of causes. Furthermore, I argue that investigations into organismal development should investigate the effects of a wider range of kinds of variation including variation in the units, modes and temporalities of development. I close by examining various possible opportunities for producing and using normal development free of the assumptions of the instructive-permissive distinction. These opportunities are afforded by recent developments, which include new ways of producing standards incorporating more natural variation and being based on function rather than structure, and the ability to produce, store, and process large quantities of data. (shrink)

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are (...) not monolithic because the opportunities and challenges presented by big data vary across scientific domains. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments (...) generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods. (shrink)

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which (...) is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power. (shrink)

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which (...) is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power. (shrink)

This article explores one aspect of digital politics, the politics of videos and more specifically of DAESH recruitment videos. It proposes a practice theoretical approach to the politics of DAESH recruitment videos focused on the re-production of regimes of (in)visibility. The article develops an argument demonstrating specifically how digital and commercial logics characterize the aesthetic, circulatory, and infrastructuring practices re-producing the regime of (in)visibility. It shows that digital/commercial logics are at the heart of the combinatorial marketing of multiple, contradictory images (...) of the DAESH polity in the videos; that they are core to the participatory, entrepreneurial, individualized and affective processes of contagion determining whom the videos reach and involve; and that they shape the sorting, linking, flagging and censoring of the videos that define their accessibility on the internet. The theoretical and political cost of overlooking these digital and commercial characteristics of DAESH visibility practices are high. It perpetuates misconceptions of how the videos work and what their politics are and it reinforces the digital Orientalism/occidentalism in which these misconceptions are anchored. (shrink)

A recent interpretation of artificial intelligence (AI) (Floridi 2013, 2022) suggests that the implementation of AI demands the investigation of the binding conditions that make it possible to build and integrate artifacts into our lived world. Such artifacts can successfully interact with the world because our environment has been designed to be compatible with intelligent machines (such as robots). As the use of AI becomes ubiquitous in society, possibly leading to the formation of increasingly intelligent bio-technological unions, there will likely (...) be a coexistence of a plethora of micro-environments wrapped and tailored around humans and basic robots. The key element of this pervasive process will be the capacity to integrate biological realms in an infosphere suitable for the implementation of AI technologies. This process will require extensive datafication. This is because data is the basis of the logical-mathematical codes and models that drive and guide AI. This process will have huge consequences on workplaces, on workers, as well as on the decision-making processes required for the functioning of future societies. In this paper we will offer a comprehensive reflection on the moral and social implications of datafication as well as a set of considerations about its desirability, which will be informed by the following insights: (1) full protection of privacy may become structurally impossible, thus leading to undesirable forms of political and social control; (2) worker’s freedom may be reduced; (3) human creativity, imagination, and even divergence from AI logic might be channeled and possibly discouraged; (4) there will likely be a push towards efficiency and instrumental reason, which will become preeminent in production lines as well as in society. (shrink)

Modern payment cards encompass a bewildering array of consumer technologies, from credit and debit cards to stored-value and loyalty cards. But what unites all of these financial media is their connection to recordkeeping systems. Each swipe sends data hurtling through invisible infrastructures to verify accounts, record purchase details, exchange funds, and update balances. With payment cards, banks and merchants have been able to amass vast archives of transactional data. This information is a valuable asset in itself. It can be used (...) for in-house data analytics programs or sold as marketing intelligence to third parties. This research examines the development of payment cards in the United States from the late 19th century to present, drawing attention to their fundamental relationship to identification, recordkeeping, and data mining. The history of payment cards, I argue, is not just a history of financial innovation and computing; it is also a history of Big Data and consumer surveillance. This history, moreover, provides insight into the growth of transactional data and the datafication of money in the digital economy. (shrink)

The article uses embodiment and the experiential basis of conceptual metaphor to argue for the metaphorical essence of abstract legal thought.concepts like ‘law’ and ‘justice’ need to borrow from a spatial, bodily, or physical prototype in order to be conceptualised, seen, for example, in the fact that justice preferably is found ‘under’ law. Three conceptual categories of how law is conceptualised is examined: law as an object, law as a vertical relation, and law as an area. The Google Ngram Viewer, (...) based on the massive library of books that Google has scanned, has been used to study legally relevant conceptions over time within each of these three categories, from 1800 to 2000. In addition, the article suggests a type of analytical method of ‘metaphor triangulation,’ that is, the replacement of prevailing metaphors with unusual ones in order to increase the level of awareness of what conceptual content the prevailing metaphors involve. (shrink)

Despite all the attention to Big Data and the claims that it represents a “paradigm shift” in science, we lack understanding about what are the qualities of Big Data that may contribute to this revolutionary impact. In this paper, we look beyond the quantitative aspects of Big Data and examine it from a sociotechnical perspective. We argue that a key factor that distinguishes “Big Data” from “lots of data” lies in changes to the traditional, well-established “control zones” that facilitated clear (...) provenance of scientific data, thereby ensuring data integrity and providing the foundation for credible science. The breakdown of these control zones is a consequence of the manner in which our network technology and culture enable and encourage open, anonymous sharing of information, participation regardless of expertise, and collaboration across geographic, disciplinary, and institutional barriers. We are left with the conundrum—how to reap the benefits of Big Data while re-creating a trust fabric and an accountable chain of responsibility that make credible science possible. (shrink)

This article compares political clientelism and datafied campaigning as two modes of relating politicians/parties and voters that are centred around voter surveillance. It contributes to the discussion on consequences of Big Data by showing similarities of datafied campaigns with a type of electoral politics that pre-dates the advent of mass media and is usually regarded as deficient. It thus departs from the predominant perspective on datafication and surveillance, which draws on Foucault, in order to identify the particular challenges that datafication (...) poses in the realm of democratic electoral politics. They are related to four major aspects in which datafied campaigning resembles political clientelism, as opposed to the combination of ideology, issue-based campaigning and media appeal that characterized Western European party politics in the second half of the 20th century. It personalizes the relationship between politicians and voters with the help of intermediaries; it is based on an asymmetric and iterative monitoring of voters; it implies a strong particularism and an affinity with populist appeals; and it is ambivalent with regard to the volition of voters. The identification of these similarities renders general concerns about the consequences of datafied campaigning for democracy more concrete. It offers a mirror in which seemingly novel practices are revealed to have implications that are well known to be problematic for the quality of democracy. (shrink)

This paper presents a review of academic literature, policy documents from government organizations and international agencies, and reports from industries and popular media on the trends in Big Data utilization in key development issues and its worthwhileness, usefulness, and relevance. By looking at Big Data deployment in a number of key economic sectors, it seeks to provide a better understanding of the opportunities and challenges of using it for addressing key issues facing the developing world. It reviews the uses of (...) Big Data in agriculture and farming activities in developing countries to assess the capabilities required at various levels to benefit from Big Data. It also provides insights into how the current digital divide is associated with and facilitated by the pattern of Big Data diffusion and its effective use in key development areas. It also discusses the lessons that developing countries can learn from the utilization of Big Data in big corporations as well as in other activities in industrialized countries. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, (...) history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

This article focuses upon defamation law in Australia and its struggles to adjust to the digital landscape, to illustrate the broader challenges involved in the governance and regulation of data associations. In many instances, online publication will be treated by the courts in a similar fashion to traditional forms of publication. What is more contentious is the question of who, if anyone, should bear the responsibility for digital forms of defamatory publication which result not from an individual author’s activity online (...) but rather from algorithmic associations. This article seeks, in part, to analyse this question, by reference to the Australian case law and associated scholarship regarding search engine liability. Reflecting on the tensions involved here offers us a fresh perspective on defamation law through the conceptual lens of data associations. Here the focus of the article shifts to explore some wider questions posed for defamation law by big data. Defamation law may come to play a significant role in emerging frameworks for algorithmic accountability, but these developments also call into question many of its traditional concepts and assumptions. It may be time to think differently about defamation and to consider its interrelationship with privacy, speech and data protection more fully. As a result, I conclude that the courts and policymakers need to engage more deeply and explicitly with the rationale for the protection of reputation and that more thought needs to be given to changing conceptions of reputation in the context of data associations. (shrink)

The relationship between politics and the digital has largely been characterized as one of epochal change. The respective theories understand the digital as external to politics and society, as an autonomous driver for global, unilateral transformation. Rather than supporting such singular accounts of the relationship between politics and the digital, this article argues for its specificity: the digital is best examined in terms of folds within existing socio-technical configurations, and as an artefact with a set of affordances that are shaped (...) and filled with meaning by social practice. In conceptualizing the digital as numeric, countable, computable, material, storable, searchable, transferable, networkable and traceable, fabricated and interpreted, it becomes clear that the digital cannot be divorced from the social. These affordances of the digital are discussed in relation to specific political, digital practices that are further developed in the different contributions in this special issue, such as predictive policing (Aradau and Blanke, this issue), data protection (Bellanova, this issue), extremist recruitment videos (Leander, this issue), political acclamation (Dean, this issue), and pandemic simulations (Opitz, this issue). (shrink)

Social practices of quantification, or the production and communication of numbers, have been recognized as important foundations of organizational knowledge, as well as sources of power. With the advent of increasingly sophisticated digital tools to capture and extract numerical data from social life, however, there is a pressing need to understand the ethical stakes of quantification. The current study examines quantification from an ethical lens, to frame and promote a research agenda around the ethics of quantification. After a brief overview (...) of quantification research and its uses in state and market organization, I discuss quantification in terms of three core subprocesses—capture, specification, and appropriation, illustrating and identifying ethical concerns around each process. Linking these processes to the performative effects of measures, I present a working model of quantification from which the discussion builds ideas for developing a research agenda around quantification. (shrink)

This paper proposes a multimodal ethnosemiotic conceptual framework for culturally themed selective Web archiving, taking as a practical example the curation of the London French Special Collection in the UK Web Archive. Its focus on a particular ‘community’ is presented as advantageous in overcoming the sheer scale of data available on the Web; yet, it is argued that these ethnographic boundaries may be flawed if they do not map onto the collective self-perception of the London French. The approach establishes several (...) theoretical meeting points between Pierre Bourdieu’s ethnography and Gunther Kress’s multimodal social semiotics, notably, the foregrounding of practice and the meaning-making potentialities of the everyday; the implications of language and categorisation; the interplay between subject and object; evolving notions of agency, authorship and audience; together with social engagement, and the archive as dynamic process and product. The curation rationale proposed stems from Bourdieu’s three-stage field analysis model, which places a strong emphasis on habitus, considered to be most accurately presented through blogs, yet necessitates its contextualisation within the broader field, through institutional websites, for example, whilst advocating a reflexive awareness of the researcher/curator’s role. This, alongside the Kressian acknowledgement of the inherent multimodality of on-line resources, lends itself convincingly to selection and valuation strategies, whilst the discussion of language, genre, authorship and audience is relevant to the potential cataloguing of Web objects. By conceptualising the culturally themed selective Web-archiving process within the ethnosemiotic framework constructed, concrete recommendations emerge regarding curation, classification and crowd-sourcing. (shrink)

The Cambridge Analytica–Facebook scandal led to widespread concern over the methods deployed by Cambridge Analytica to target voters through psychographic profiling algorithms, built upon Facebook user data. The scandal ultimately led to a record-breaking $5 billion penalty imposed upon Facebook by the Federal Trade Commission in July 2019. The FTC action, however, has been criticized as failing to adequately address the privacy and other harms emanating from Facebook’s release of approximately 87 million Facebook users’ data, which was exploited without user (...) authorization. This Essay summarizes the FTC’s response to the Cambridge Analytica–Facebook scandal. It concludes that the scandal focuses attention on the need to explore the potential for embedding due process-type inquiries and protections within the enforcement actions by regulatory agencies such as the FTC. These protections are increasingly important in addressing the problem of “black boxing the voter” that is now presented by data- and algorithmic-driven companies such as Cambridge Analytica and Facebook. (shrink)

Insurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients’ behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and Science and Technology Studies, this article investigates the role of economic (...) experimentation in the making of data-driven personalisation markets in insurance. We document a case study of a car insurance experiment, launched by a Belgian direct insurance company in 2016 to set up an experiment of tracking driving style behavioural data of over 5000 participants over a one-year period. Based on interviews and document analysis, we outline how this in vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Using JL Austin’s distinction between happy and unhappy statements, we argue how the experiment, despite its failure not to provide the desired evidence, could be considered a ‘happy’ event. We conclude by highlighting the role of economic experiments ‘in the wild’ for the making of future markets of data-driven personalisation. (shrink)

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on first principles, and the naïve-inductivist one, based only on data. This latter view has recently gained some attention in response to the availability (...) of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann. (shrink)

Until the end of the last century, media sociology was synonymous with the investigation of mass media as a social domain. Today, media sociology needs to address a much higher level of complexity, that is, a deeply mediatized world in which all human practices, social relations, and social order are entangled with digital media and their infrastructures. This article discusses this shift from a sociology of mass communication to the sociology of a deeply mediatized world. The principal aim of the (...) article is to outline a new media-sociological imagination: media sociology as a cross-sectional sociology, a sociology of entanglement, and a new critical sociology of technological deep structures. (shrink)