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)

One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat (...) to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions. It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation. (shrink)

Science and technology influence international affairs by many different mechanisms. Both create new issues, risks and uncertainties. Advances in science alert the international community to new issues and risks. New technological capabilities transform war, diplomacy, commerce, intelligence, and investment. This paper identifies six basic patterns by which advances in science and technology influence international relations: as a juggernaut or escaped genie with rapid and wide-ranging ramifications for the international system; as a game-changer and a conveyer of advantage and disadvantage to (...) different actors in the international system; as a source of risks, issues and problems that must be addressed and managed by the international community; as key dimensions or enablers of international macro phenomena; as instruments of foreign policy or sources of technical information for the management of an ongoing international regime; as the subject of projects and institutions whose planning, design, implementation and management provide grist for the mill of international relations and diplomacy. (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)

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)

Many states make use of personal identity numbers to govern people living in their territory and jurisdiction, but only a few rely on an all-purpose PIN used throughout the public and private sectors. This article examines the all-purpose PIN in Finland as a political technology that brings people to the sphere of public welfare services and subjects them to governance by public authorities and expert institutions. Drawing on documentary materials and interviews, it unpacks the history and uses of the PIN (...) as an elementary building block of the Nordic welfare state, and its emerging uses in the post-welfare data economy. The article suggests that, although the PIN is capable of individualizing, identifying, and addressing individuals, its most important and widely embraced feature is the extent to which it enables interoperability among public authorities, private businesses, and their data repositories. Interoperability, together with advances in computing and information technology, has made the PIN a facilitator of public administration, state knowledge production, and everyday life. More recently, in the post-welfare data economy, interoperability has rendered the PIN a national asset in all the Nordic countries, providing a great advantage to biomedical research, innovation business, and healthcare. (shrink)

Bold approaches to data collection and large-scale quantitative advances have long been a preoccupation for social science researchers. In this commentary we further debate over the use of large-scale survey data and official statistics with ‘Big Data’ methodologists, and emphasise the ability of these resources to incorporate the essential social and cultural heredity that is intrinsic to the human sciences. In doing so, we introduce a series of new data-sets that integrate approximately 30 years of survey data on victimisation, fear (...) of crime and disorder and social attitudes with indicators of socio-economic conditions and policy outcomes in Britain. The data-sets that we outline below do not conform to typical conceptions of ‘Big Data’. But, we would contend, they are ‘big’ in terms of the volume, variety and complexity of data which has been collated and ‘big’ also in that they allow us to explore key questions pertaining to how social and economic policy change at the national level alters the attitudes and experiences of citizens. Importantly, they are also ‘small’ in the sense that the task of rendering the data usable, linking it and decoding it, required both manual processing and tacit knowledge of the context of the data and intentions of its creators. (shrink)

This article presents a conceptual investigation into the value impacts and relations of algorithms in the domain of justice and security. As a conceptual investigation, it represents one step in a value sensitive design based methodology. Here, we explicate and analyse the expression of values of accuracy, privacy, fairness and equality, property and ownership, and accountability and transparency in this context. We find that values are sensitive to disvalue if algorithms are designed, implemented or deployed inappropriately or without sufficient consideration (...) for their value impacts, potentially resulting in problems including discrimination and constrained autonomy. Furthermore, we outline a framework of conceptual relations of values indicated by our analysis, and potential value tensions in their implementation and deployment with a view towards supporting future research, and supporting the value sensitive design of algorithms in justice and security. (shrink)

Building on conceptual insights from the history and sociology of numbers, media and surveillance studies, and theories of governance and risk, this article analyzes the forms of transparency produced by the use of numbers in social life. It examines what it is about numbers that often makes their ‘truth claims’ so powerful, investigates the role that numerical operations play in the production of retrospective, real-time and anticipatory forms of transparency in contemporary politics and economic transactions, and discusses some of the (...) implications resulting from the increasingly abstract and machine-driven use of numbers. It argues that the forms of transparency generated by machine-driven numerical operations open up for individual and collective practices in ways that are intimately linked to precautionary and pre-emptive aspirations and interventions characteristic of contemporary governance. As such, these numerical operations raise important political and ethical questions that deserve further conceptual and empirical scrutiny. (shrink)

Cybervetting has become common practice in personnel decision-making processes of organizations. While it represents a quick and inexpensive way of obtaining additional information on employees and applicants, it gives rise to a variety of legal and ethical concerns. To limit companies’ access to personal information, a _right to be forgotten_ has been introduced by the European jurisprudence. By discussing the notion of forgetting from the perspective of French hermeneutic philosopher Paul Ricoeur, the present article demonstrates that both, companies and employees, (...) would be harmed if access to online information on applicants and current employees would be denied. Consistent with a Humanistic Management approach that promotes human dignity and flourishing in the workplace, this article proposes guidance for the responsible handling of unpleasant online memories in personnel decision-making processes, thereby following Ricoeur’s notion of forgetting as “kept in reserve”. Enabling applicants and employees to take a qualified stand on their past is more beneficial to both sides than a right to be forgotten that is questionable in several respects. (shrink)

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering it (...) incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency. (shrink)

La filosofía de la comunicación del siglo XX aceptó, en términos generales, la teoría de la agenda-setting propuesta por McCombs y Shaw (1972). Esta teoría subrayaba la capacidad de los medios de comunicación de masas para configurar la comprensión que el gran público tiene de la realidad social (Wolf, 1987). Sin embargo, Internet y las redes sociales han cambiado radicalmente el panorama, puesto que los grandes medios de comunicación tradicionales van ahora a remolque de lo que es trending-topic en las (...) redes. Este artículo señala la necesidad de desarrollar una nueva filosofía de la comunicación, adaptada a la existencia de un nuevo marco (frame) tecnológico y social, que redefina los patrones y los factores que configuran actualmente la agenda pública (agenda-setting 2.0). (shrink)

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are (...) discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission—aiding infection control measures and policies—emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: automation and algorithmic reliance impacting freedom of choice, big data analytics complexity impacting informed consent, reliance on profiling impacting individual and group identities and justice/fair access and increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement. (shrink)

The advent of extremely large data sets, known as “big data,” has been heralded as the instantiation of a new science, requiring a new kind of practitioner: the “data scientist.” This article explores the concept of big data, drawing attention to a number of new issues—not least ethical concerns, and questions surrounding interpretation—which big data sets present. It is observed that the skills required for data scientists are in some respects closer to those traditionally associated with the arts and humanities (...) than to those associated with the natural sciences; and it is urged that big data presents new opportunities for dialogue, especially concerning hermeneutical issues, for theologians and data scientists. (shrink)

Metrics have long served as tools for governing at a distance. In the food industry, major manufacturers have embraced metrics as tools to govern the sustainability of the farms producing their commodity raw materials. This metrical turn has been influenced but also complicated by agricultural datafication, that is, the increasing quantities of data generated on and about farms. Despite the sheer abundance of data that companies might use to measure and drive improvement in on-farm sustainability, they have struggled to collect (...) data suitable for such purposes. Attention to the different kinds of distance and diversity across which metrics are supposed to govern suggests reasons why they may fail to do so, even when wielded by otherwise powerful corporations. (shrink)

To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods—termed ’property descriptors’—that illuminate not just (...) the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding. (shrink)

Today's modern car is an assemblage of mechanical and digital components, of metal panels that comprise its structure and silicon chips that run its functions. Communication and information studies scholars have interrogated the problematic aspects of the programs that run those functions, revealing serious issues surrounding privacy and security, worker surveillance, and racial, gendered, and class-based bias. This article contributes to that work by taking a step back and asking about the issues inherent not in the software running on these (...) chips, but on the microchips themselves. Using the lens of “chipification”—the process by which a device is rendered capable of reading and processing data through the embedding of microchips—this article explores how the integration of microchips also involves processes that are often borne from, replicate, or create troubling power dynamics of their own. It takes light-duty passenger vehicles as a case study into how chipification is radically reshaping such processes as resource allocation, labor flows, and cultural practices around car manufacture, use, repair, and modification. By naming the process of chipification, this article allows researchers to identify and analyze the ways that integrating data processing capabilities into everyday devices is not a frictionless practice, but rather one that implicates a variety of power dynamics within these massive industries. (shrink)

Although true in some aspects, the suggested characterization of today’s science as a dichotomy between traditional science and data-driven science misses some of the nuance, complexity, and possibility that exists between the two positions. Part of the problem is the claim that Data Science works without theories. There are many theories behind the data that are used in science. However, for data science, the only theories that matter are those in mathematics, statistics, and computer science. In this conceptual paper, we (...) add two other philosophy of science tenets, experiments and data, to the discussion to create a more nuanced view of how data science uses theories. Following Ihde’s concept of technoscience and the incessant quest for more precision, magnification, and resolution, we argue that technology-driven science created a need for more technology-driven science, culminating in data science. Further, we adapt Hacking and Galison’s views on physics to argue that data science is also an experimental science, which uses data objects in experiments. Drawing from Heelan, we called these objects “data-objects-for-knowing”. Finally, we conclude that data science is a science to study artificially created phenomena—a science to study the data manipulated by the equations and operations of AI. It disregards the connections between data and the real world that were carefully built by the theories from other sciences. In the experiments of data science, data are the world itself. The knowledge created by data science is purposely disconnected from any theory from other sciences; it is a knowledge for the sake of itself. (shrink)

While we witness a growing belief in transparency as an ideal solution to a wide range of societal problems, we know less about the practical workings of transparency as it guides conduct in organizational and regulatory settings. This article argues that transparency efforts involve much more than the provision of information and other forms of ‘sunlight’, and are rather a matter of managing visibilities than providing insight and clarity. Building on actor-network theory and Foucauldian governmentality studies, it calls for careful (...) attention to the ways in which transparency ideals are translated into more situated practices and become associated with specific organizational and regulatory concerns. The article conceptualizes transparency as a force that shapes conduct in organizational and socio-political domains. In the second section, this conceptualization of transparency as a form of ‘ordering’ is substantiated further by using illustrations of the effects of transparency efforts in the internet domain. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems (...) medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)

The debate on the right to be forgotten on Google involves the relationship between human information processing and digital processing by algorithms. The specificity of digital memory is not so much its often discussed inability to forget. What distinguishes digital memory is, instead, its ability to process information without understanding. Algorithms only work with data without remembering or forgetting. Merely calculating, algorithms manage to produce significant results not because they operate in an intelligent way, but because they “parasitically” exploit the (...) intelligence, the memory, and the attribution of meaning by human actors. The specificity of algorithmic processing makes it possible to bypass the paradox of remembering to forget, which up to now blocked any human-based forgetting technique. If you decide to forget some memory, the most immediate effect is drawing attention to it, thereby activating remembering. Working differently from human intelligence, however, algorithms can implement, for the first time, the classical insight that it might be possible to reinforce forgetting not by erasing memories but by multiplying them. After discussing several projects on the web which implicitly adopt this approach, the article concludes by raising some deeper problems posed when algorithms use data and metadata to produce information that cannot be attributed to any human being. (shrink)

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil. We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest (...) data diaries are able to capture the informal dimension of data-intensive organisations; enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data. (shrink)

Fighting crime has historically been a field that drives technological innovation, and it can serve as an example of different governance styles in societies. Predictive policing is one of the recent innovations that covers technical trends such as machine learning, preventive crime fighting strategies, and actual policing in cities. However, it seems that a combination of exaggerated hopes produced by technology evangelists, media hype, and ignorance of the actual problems of the technology may have boosted sales of software that supports (...) policing by predicting offenders and crime areas. In this paper we analyse currently used predictive policing software packages with respect to common problems of data mining, and describe challenges that arise in the context of their socio-technical application. (shrink)

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare (...) field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare. (shrink)

In recent years, the healthcare field welcomed an emerging field of practices captured under the umbrella term ‘Big Data’. This term is surrounded with positive rhetoric and promises about the ability to analyse real-world data quickly and comprehensively. Such rhetoric is highly consequential in shaping debates on Big Data. While the fields of Science and Technology Studies and Critical Data Studies have been instrumental in elaborating the neglected and problematic dimensions of Big Data, it remains an open question how and (...) to what extent such insights become embedded in other fields. In this paper, we analyse the epistemological claims that accompany Big Data in the healthcare domain. We systematically searched scientific literature and selected 206 editorials as these reflect on developments in the domain. Through an interpretive analysis, we construct five ideal-typical discourses that all frame Big Data in specific ways. Three of the discourses frame Big Data in positive terms and disseminate a compelling rhetoric. Metaphors of ‘capturing’, ‘illuminating’ and ‘harnessing’ data presume that Big Data are benign and leading to valid knowledge. The scientist and critical-interpretive discourses question the objectivity and effectivity claims of Big Data. Metaphors of ‘selecting’ and ‘constructing’ data illustrate another political message, framing Big Data as limited. We conclude that work in the critical-interpretive discourse has not broadly infiltrated the medical domain. Ways to better integrate aspects of the discourse in the healthcare domain are urgently needed. (shrink)

The rise of Big Data changes the context in which organisations producing official statistics operate. Big Data provides opportunities, but in order to make optimal use of Big Data, a number of challenges have to be addressed. This stimulates increased collaboration between National Statistical Institutes, Big Data holders, businesses and universities. In time, this may lead to a shift in the role of statistical institutes in the provision of high-quality and impartial statistical information to society. In this paper, the changes (...) in context, the opportunities, the challenges and the way to collaborate are addressed. The collaboration between the various stakeholders will involve each partner building on and contributing different strengths. For national statistical offices, traditional strengths include, on the one hand, the ability to collect data and combine data sources with statistical products and, on the other hand, their focus on quality, transparency and sound methodology. In the Big Data era of competing and multiplying data sources, they continue to have a unique knowledge of official statistical production methods. And their impartiality and respect for privacy as enshrined in law uniquely position them as a trusted third party. Based on this, they may advise on the quality and validity of information of various sources. By thus positioning themselves, they will be able to play their role as key information providers in a changing society. (shrink)

The legal basis for processing personal data and some other types of Big Data is often the informed consent of the data subject involved. Many data controllers, such as social network sites, offer terms and conditions, privacy policies or similar documents to which a user can consent when registering as a user. There are many issues with such informed consent: people get too many consent requests to read everything, policy documents are often very long and difficult to understand and users (...) feel they do not have a real choice anyway. Furthermore, in the context of Big Data refusing consent may not prevent predicting missing data. Finally, consent is usually asked for when registering, but rarely is consent renewed. As a result, consenting once often implies consent forever. At the same time, given the rapid changes in Big Data and data analysis, consent may easily get outdated. This paper suggests expiry dates for consent, not to settle questions, but to put them on the table as a start for further discussion on this topic. Although such expiry dates may not solve all the issues of informed consent, they may be a useful tool in some situations. (shrink)

TheCatalogue of Scientific Papers, published by the Royal Society of London beginning in 1867, projected back to the beginning of the nineteenth century a novel vision of the history of science in which knowledge was built up out of discrete papers each connected to an author. Its construction was an act of canon formation that helped naturalize the idea that scientific publishing consisted of special kinds of texts and authors that were set apart from the wider landscape of publishing. By (...) recovering the decisions and struggles through which theCataloguewas assembled, this essay aims to contribute to current efforts to denaturalize the scientific paper as the dominant genre of scientific life. By privileging a specific representation of the course of a scientific life as a list of papers, theCataloguehelped shape underlying assumptions about the most valuable fruits of a scientific career. Its enumerated lists of authors’ periodical publications were quickly put to use as a means of measuring scientific productivity and reputation, as well as by writers of biography and history. Although it was first conceived as a search technology, this essay locates theCatalogue’s most consequential legacy in its uses as a technology of valuation. (shrink)

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

The proliferation of digital data and internet-based research technologies is transforming the research landscape, and researchers and research ethics communities are struggling to respond to the ethical issues being raised. This paper discusses the findings from a collaborative project that explored emerging ethical issues associated with the expanding use of digital data for research. The project involved consulting with researchers from a broad range of disciplinary fields. These discussions identified five key sets of issues and informed the development of guidelines (...) orientated to meet the needs of researchers and ethics committee members. We argue that establishing common approaches to assessing ethical risks of research involving digital data will promote consistency in the ethical standards for research, enable the smooth functioning of ethics committees, and sustain public confidence in research. We conclude with recommendations for the development of educational resources for ethics committees, data management guidelines and further public education. (shrink)

Big Data evangelists often argue that algorithms make decision-making more informed and objective—a promise hotly contested by critics of these technologies. Yet, to date, most of the debate has focused on the instruments themselves, rather than on how they are used. This article addresses this lack by examining the actual practices surrounding algorithmic technologies. Specifically, drawing on multi-sited ethnographic data, I compare how algorithms are used and interpreted in two institutional contexts with markedly different characteristics: web journalism and criminal justice. (...) I find that there are surprising similarities in how web journalists and legal professionals use algorithms in their work. In both cases, I document a gap between the intended and actual effects of algorithms—a process I analyze as “decoupling.” Second, I identify a gamut of buffering strategies used by both web journalists and legal professionals to minimize the impact of algorithms in their daily work. Those include foot-dragging, gaming, and open critique. Of course, these similarities do not exhaust the differences between the two cases, which are explored in the discussion section. I conclude with a call for further ethnographic work on algorithms in practice as an important empirical check against the dominant rhetoric of algorithmic power. (shrink)

This article focuses on key roles that the ill-defined concept of ‘public benefit’ plays in accessing the public health data held by the UK’s National Health Service. Using the concept of the ‘trade-off fallacy’, this article argues that current data access and governance structures, based on particular construals of public benefit in the context of public health data, largely negate the possibility of effective control by individuals over future uses of personal health data. This generates a health data version of (...) the trade-off fallacy that enables widespread involvement of commercial actors in personal data, despite public concerns over commercial involvement in, and potential exploitation of, public health data. The article suggests that, despite ostensibly robust regulatory and governance structures, this publicly held data is potentially subject to similar logics of accumulation as seen elsewhere in the digital economy, highlighting the inadequacies of current data regulatory frameworks in the digital era. (shrink)

The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis (...) with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An examination of the current state of research on models with telematics data shows however that the epistemological leap, for now, has not happened. (shrink)

Fleur Johns argues that the contraposition of a ‘bottom-up’ approach of politics of prototypical technique rather than the ‘top-down’ politics of the master plan or normative principle no longer seems as straightforwardly radical as it appeared when James C Scott posited the value of local knowledge or métis against grand plans of high modernization, just over 20 years ago. This paper seeks to follow Johns’ call, ‘to capture and probe some of the effects of sensibility, rationality or style widely reproduced (...) in the details of development work’. It draws upon fieldwork in Nairobi to open up a discussion of a shift in sensibility from a ‘bottom-up’ or ‘postliberal’ approach to a framing of open-ended encounter. The paper critiques this imaginary of relational encounter by engaging contemporary work in critical black studies. It suggests that the problem of critique is that it reproduces the problems of governing imaginaries, continually seeking to rework the human subject via adaptive capacities, sensitivities to difference and openness to alterity, while leaving intact the coloniality of being, the antiblack world. (shrink)

This article is organized in four sections. The first section introduces sensing governance in terms of the governance of effects rather than causation, focusing on the work of Bruno Latour in establishing the problematic of contingent interaction, rather than causal depth, as key to emergent effects, which can be unexpected and catastrophic. The second section considers in more depth how sensing governance enables politics by other means through putting greater emphasis on relations of interaction, rather than on ontologies of being, (...) and links the methodological approach of sensing governance closely to actor network assumptions that disavow structures of causation. The final two sections analyze how correlation works to reveal new agencies and processes of emergence and how new technologies have been deployed in this area, providing some examples of how the shift from causal relations to sensing effects has begun to alter governmental approaches. (shrink)

During the 2020 COVID-19 pandemic, open science has become central to experimental, public health, and clinical responses across the globe. Open science is described as an open commons, in which a right to science renders all possible scientific data for everyone to access and use. In this common space, capitalist platforms now provide many essential services and are taking the lead in public health activities. These neoliberal businesses, however, have a problematic role in the capture of public goods. This paper (...) argues that the open commons is a community of rights, consisting of people and institutions whose interests mutually support the public good. If OS is a cornerstone of public health, then reaffirming the public good is its overriding purpose, and unethical platforms ought to be excluded from the commons and its benefits. (shrink)

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

This article conducts a brief review of works dealing with recognition processes in media environments, with a special focus on social media platforms. It argues that efforts to analyze dynamics of recognition in datafied spaces should take into consideration the working logics of such platforms, which are responsible for the organization of media practices around the creation of economic value for the companies. The article examines the news feeds as a type of social space where these logics are manifested in (...) the decisions of the platforms about what and when to show to their users. In this context, practices of sociability aimed at increasing recognition in social media platforms should be understood within a broader movement of datafication of society, which deepens the presence of capitalist logics in social life. (shrink)

Cities, such as industry or the universities, are immersed in a process of digital transformation generated by the possibility and technological convergence of the Internet of Things, Big Data and Artificial Intelligence and its consequences: hyperconnectivity, datafication and algorithmization. A process of transformation towards what has come to be called as Smart Cities. The aim of this paper is to show the impacts and consequences of digital connectivity, algorithmization and the datafication of urban digital society to outline possible ways of (...) resolving the underlying moral conflicts. (shrink)

A review of Gary Smith and Jay Cordes: The Phantom Pattern Problem: The Mirage of Big Data. New York: Oxford University Press, 2020.