Some observers of the data economy have proposed that we treat data as labor. But are data contributions labor? Our folk conception of work emphasizes its importance and effort, such that work has a special interpersonal priority and deserves appreciation and compensation. The folk conception does not generally favor counting data as work, and so it serves as an error theory for reluctance to regulate data as labor. In contrast, labor regulation and policy focus on the political economy of labor, (...) and in particular the bargaining dynamics that participants in a labor market face. Labor regulation aims to protect workers against the threats characteristically posed by such bargaining dynamics. Data-transferring interactions between internet platforms and users share similar bargaining dynamics, and so there is a promising functional similarity between labor regulation and proposals for data regulation that would facilitate data strikes and data unions. (shrink)

The right to privacy extends only to information through which the persons concerned are identifiable. This assumption is widely shared in law and in philosophical debate; it also guides the handling of personal data, for example, in medicine. However, this essay argues that the dissemination of anonymous information can also constitute a violation of privacy. This conclusion arises from two theses: (1) From the perspective of the affected person, judgments by others about anonymous information refer to its originator, even if (...) outsiders do not know who it is; (2) Our self-understanding is co-constituted by the judgment of others about us. This is a consequence of our social nature. There are certain areas of our lives that we do not want to expose to the gaze of others for our own sake. Therefore, the unauthorized dissemination of information concerning these areas can violate our privacy even if they are anonymous. While the question of the extent of privacy poses a general problem, there are reasons to discuss it in the context of digital medicine. (shrink)

With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources (...) of dispute. The lens of this typology then allows us to look anew at the potential biases in insurance pricing implied by big data and machine learning, showing that despite utopic claims, social stereotypes continue to plague data, thus threaten to unconsciously reproduce these discriminations in insurance. To counter these effects, algorithmic fairness attempts to define mathematical indicators of non-bias. We argue that this may prove insufficient, since as it assumes the existence of specific protected groups, which could only be made visible through public debate and contestation. These are less likely if the right to explanation is realized through personalized algorithms, which could reinforce the individualized perception of the social that blocks rather than encourages collective mobilization. (shrink)

In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings.

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data – the so-called ‘Big Data’ – can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasi-false and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as a total structure, or (...) as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. (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)

Risk is an intrinsic part of our lives. In the future, the development and growth of the Internet of things allows getting a huge amount of data. Considering this evolution, our research focuses on developing a novel concept, namely Holistic Risk Assessment (HRA), that takes into consideration elements outside the direct influence of the individual to provide a highly personalized risk assessment. The HRA implies developing a methodology and a model. This paper is related to the epistemological positioning of this (...) research. We consider this research as an artificial science under the constructivism paradigm. We also introduce the positioning of this research in the complexity science paradigm. We end this paper by presenting a methodology based on an adapted Design Thinking Model. (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)

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)

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)

Qual o lugar da filosofia nos dias atuais? Diante das inúmeras respostas possíveis a esta questão, nos debruçaremos em alguns tópicos que podemos inserir na chamada Ética Informacional, um ramo de investigação filosófico-interdisciplinar relativamente recente que discute problemas oriundos da relação ser humano/tecnologias digitais. Temas como privacidade informacional, arrogância epistêmica e divisão digital serão discutidos e relacionados, com o intuito de ilustrar o papel da filosofia na compreensão da complexidade inerente às dinâmicas sociais no contexto da sociedade da informação. Argumentaremos (...) que pensar o mundo contemporâneo a partir do viés da ética informacional se faz cada dia mais imprescindível para que possamos entrar em sintonia com as rápidas mudanças que a revolução informacional impõe à vida em sociedade e não sermos levados pelas enxurradas informacionais que presenciamos diariamente. (shrink)

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are in tension with a (...) prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (shrink)

Contemporary privacy theories and European discussions about data protection employ the notion of ‘personal information’ to designate their areas of concern. The notion of personal information is demarcated from non-personal information—or just information—indicating that we are dealing with a specific kind of information. However, within privacy scholarship the notion of personal information appears undertheorized, rendering the concept somewhat unclear. We argue that in an age of datafication, protection of personal information and privacy is crucial, making the understanding of what is (...) meant by ‘personal information’ more important than ever. To contribute to this aim, we analyse the conception of personal information and its nature, including the distinction between personal and non-personal information from a philosophy of language perspective. Through analyses of aboutness and relative aboutness we point to challenges related to the demarcation between personal and non-personal information, which may in practice lead to all information being personal. (shrink)

Slightly modified excerpt from the section 13.4 Zusammenfassung und Ausblick (translated into englisch): This chapter is based on an analysis of ethical debates on epigenetics and genome editing, debates, in which ethical arguments relating to future generations and justice play a central role. The analysis aims to contextualize new developments in genetic engineering, such as genome and epigenome editing, ethically. At the beginning, the assumptions of "genetic determinism," on which "genetic essentialism" is based, of "epigenetic determinism" as well as "genetic" (...) and "epigenetic exceptionalism" are analyzed and critically discussed. The remarks on the ethical discourse on epigenetics show that the notion of "epigenetic determinism" can sometimes be discerned not only in popular scientific discourse but also in ethical discourse. Ethical debates on epigenetics, however, have distanced themselves from this deterministic understanding. As a result, the focus in the ethical discourse on epigenetics shifts from responsibility for one's own health and that of subsequent generations to justice. What is meant here is justice, for example, in terms of access to healthy environmental conditions, regardless of whether these contribute to health with or without epigenetic agency. An analysis of the discourse on genome editing reveals that it is primarily germline interventions that are being ethically examined, with a concentration on the aspect of inheritance. The question is whether this is accompanied by an implicit "genetic determinism" or even a "genetic essentialism": the determinism could lie in the centrality of the aspect of heritability, since only genetic information is inherited. Does the aspect of heritability and genome modification play a more decisive role in debates on genome editing than the safety issue? Is the problem of embryos and their potential offspring not being able to consent to germline intervention given such a high priority because it is a genetic intervention? Under such a premise of "strong genetic determinism" supplemented by "epigenetic determinism," not only genome editing but also epigenome editing would be ethically relevant precisely because it too would have an influence on the genome. How this influence of genome editing and epigenome editing is ethically evaluated in each case therefore depends initially on whether the assumptions of "genetic" and "epigenetic determinism" are advocated. These assumptions are increasingly viewed critically in ethical discourse because they cannot be confirmed from a scientific perspective. In popular scientific debates in particular, however, they seem to persist, which ultimately also influences the public discussion. Since a broad public discussion is required especially on genome editing, a reflective approach to the various "-isms" analyzed in this chapter is central. DOI: 10.5771/9783748927242. (shrink)

In this paper, we examine how the availability of massive quantities of data i.e., the “Big Data” phenomenon, contributes to the creation, spread, and harms of online misinformation. Specifically, we argue that a factor in the problem of online misinformation is the evolved human instinct to recognize patterns. While the pattern-recognition instinct is a crucial evolutionary adaptation, we argue that in the age of Big Data, these capacities have, unfortunately, rendered us vulnerable. Given the ways in which online media outlets (...) profit from the spread of misinformation by preying on this pattern-finding instinct, we conceptualize the problem that we identify as a morally objectionable form of “epistemic exploitation.” As we argue, the consumer of digital misinformation is often exploited by having her pattern-recognition instinct used against her. This exploitation is morally objectionable because it deprives her of an epistemic good to which she has a right. This epistemic good is the integrity of the pattern-recognition instinct itself which, we argue, is a capacity that allows us to participate in uniquely human goods. While our primary goal is to bring attention to this form of epistemic exploitation, we conclude by briefly evaluating some general solutions to the growing problem of online misinformation. (shrink)

When radiocarbon dating techniques were applied to archaeological material in the 1950s they were hailed as a revolution. At last archaeologists could construct absolute chronologies anchored in temporal data backed by immutable laws of physics. This would make it possible to mobilize archaeological data across regions and time-periods on a global scale, rendering obsolete the local and relative chronologies on which archaeologists had long relied. As profound as the impact of 14C dating has been, it has had a long and (...) tortuous history now described as proceeding through three revolutions, each of which addresses distinct challenges of capturing, processing and packaging radiogenic data for use in resolving chronological puzzles with which archaeologists has long wrestled. In practice, mobilizing radiogenic data for archaeological use is a hard-won achievement; it involves multiple transformations that, at each step of the way, depend upon a diverse array of technical expertise and background knowledge. I focus on strategies of triangulation and traceability that establish the integrity of these data and their relevance as anchors for evidential reasoning in archaeology. (shrink)

This paper discusses the epistemic status of biology from the standpoint of the systemic approach to living systems based on the notion of biological autonomy. This approach aims to provide an understanding of the distinctive character of biological systems and this paper analyses its theoretical and epistemological dimensions. The paper argues that, considered from this perspective, biological systems are examples of emergent phenomena, that the biological domain exhibits special features with respect to other domains, and that biology as a discipline (...) employs some core concepts, such as teleology, function, regulation among others, that are irreducible to those employed in physics and chemistry. It addresses the claim made by Jacques Monod that biology as a science is marginal. It argues that biology is general insofar as it constitutes a paradigmatic example of complexity science, both in terms of how it defines the theoretical object of study and of the epistemology and heuristics employed. As such, biology may provide lessons that can be applied more widely to develop an epistemology of complex systems. (shrink)

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

This paper focuses on Precision Public Health (PPH), described in the scientific literature as an effort to broaden the scope of precision medicine by extrap- olating it towards public health. By means of the “All of Us” (AoU) research pro- gram, launched by the National Institutes of Health in the U.S., PPH is being devel- oped based on health data shared through a broad range of digital tools. PPH is an emerging idea to harness the data collected for precision medicine (...) to be able to tai- lor preventive interventions for at-risk groups. The character of these data concern genetic identity, lifestyle and overall health and therefore affect the ‘intimacy’ of personhood. Through the concept of biological citizenship, we elucidate how AoU and its recruitment tactics, by resonating ‘diversity’, at the same time appeal to and constitute identity, defining individuals as ‘data sharing subjects’. Although PPH is called for; the type of bio-citizenship that is enacted here, has a particular definition, where participant recruitment focuses on ‘citizenship’ in terms of empowerment (front), it is the ‘bio’ prefix that has become the main focus in terms of research. i.e. biosubjectivities vs biocapital. This raises the question whether the societal chal- lenges that often underlie public health issues can be sufficiently dealt with based on the way ‘diversity’ is accounted for in the program. We suggest that the AoU still risks of harming underrepresented groups based on the preconditions and the design of the program. (shrink)

An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further still to (...) algorithmic processes of evaluations which will make authoritative claims as to the workers’ productivity. Furthermore, in and out of the workplace, the worker is governed by an invisible data-created leash deploying wearable technology to collect intimate worker data. At all stages, the worker is confronted with a lack of transparency, accountability, or explanation as to the inner workings or even the logic of the “black box” at work. This data revolution of the workplace is alarming for several reasons: the “black box at work” not only serves to conceal disparities in hiring, but could also allow for a level of “data-laundering” that beggars any notion of equal opportunity in employment and there exists, the danger of a “mission creep” attitude to data collection that allows for pervasive surveillance, contributing to the erosion of both the personhood and autonomy of workers. Thus, the “black box at work” not only enables worker domination in the workplace, it deprives the worker of Rawlsian justice. (shrink)

The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community (...) itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation. (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)

Ever since Big Data became a mot du jour across social fields, optical metaphors such as the microscope began to surface in popular discourse to describe and qualify its epistemological impact. While the persistence of optics seems to be at odds with the datafication of vision, this article suggests that the optical metaphor offers an opportunity to reflect about the material consequences of the modes of seeing and knowing that currently shape datafied worlds. Drawing on feminist new materialism, the article (...) investigates the optical metaphor as a material-discursive practice that actively constitutes the world, as metaphors imply modes of thinking, knowing and doing that have material enactions. Expanding visual culture theories, the notion of ‘optical unconscious’ is taken up to discuss the tensions between displacement and persistence of optics within datafied worlds, that is, how optical vision is displaced but also mobilised and repurposed by data-driven knowledge. In dialogue with feminist science and technology studies and speculative ethics, I suggest that the datafication of vision offers a chance to reconceptualize the sense of sight towards a sensorial engagement with Big Data premised on responsibility, care, and an ethics of unknowability. Within this framework, vision may be conceived differently, perhaps not only as enhancement and control, but as generator of new possibilities. Ultimately, the article proposes that the visual theories after which Big Data is being imagined matter not only for our understanding of Big Data's epistemic potential, but also for the possibility of shaping emerging data worlds. (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)

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)

The claim is frequently made that, as cities become loaded up with information and communications technology and a resultant profusion of data, so they are becoming sentient. But what might this mean? This paper offers some insights into this claim by, first of all, reworking the notion of the social as a spatial complex of ‘outstincts’. That makes it possible, secondly, to reconsider what a city which is aware of itself might look like, both by examining what kinds of technological (...) practices are becoming commonplace and by considering the particular case of spatial awareness. In turn, this leads to a third rumination on how cities might become aware as different kinds of sprite, channelling outstincts in spatially variable ways. Whatever the case, it is clear that new technical-artistic interventions are required if these sprites are not to become simply servants of the security–entertainment complex. Some of these interventions are examined in the fourth part of the paper. (shrink)

This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ (...) and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness. (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)

Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

This paper builds off the Our Data Ourselves research project, which examined ways of understanding and reclaiming the data that young people produce on smartphone devices. Here we explore the growing usage and centrality of mobiles in the lives of young people, questioning what data-making possibilities exist if users can either uncover and/or capture what data controllers such as Facebook monetize and share about themselves with third-parties. We outline the MobileMiner, an app we created to consider how gaining access to (...) one’s own data not only augments the agency of the individual but of the collective user. Finally, we discuss the data making that transpired during our hackathon. Such interventions in the enclosed processes of datafication are meant as a preliminary investigation into the possibilities that arise when young people are given back the data which they are normally structurally precluded from accessing. (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)

This introduction to the special issue on data and agency argues that datafication should not only be understood as the process of collecting and analysing data about Internet users, but also as feeding such data back to users, enabling them to orient themselves in the world. It is important that debates about data power recognise that data is also generated, collected and analysed by alternative actors, enhancing rather than undermining the agency of the public. Developing this argument, we first make (...) clear why and how the question of agency should be central to our engagement with data. Subsequently, we discuss how this question has been operationalized in the five contributions to this special issue, which empirically open up the study of alternative forms of datafication. Building on these contributions, we conclude that as data acquire new power, it is vital to explore the space for citizen agency in relation to data structures and to examine the practices of data work, as well as the people involved in these practices. (shrink)

Access to data in crime and place research has traditionally been reserved for those who have the means to collect fresh data themselves, pay for access, or obtain data through formal data sharing agreements. Even when access is granted, the usage of these data often comes with conditions that circumscribe how the data can be used through licensing or policy (Kitchin, 2014). Even the public dissemination of findings which emerge from analysis might be subject to restrictions. This can lead to (...) unequal access, controlled usage and curb the diffusion of findings, severely limiting the insight that can be obtained from data. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning (...) analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

Our technological lifeworld has become an info-computational media populated by data and algorithms, an artificial environment for life and shared experiences. In this chapter, I tried to sketch three new assumptions for bioethics – it is hardly possible to substantiate ethical guidelines or an idea of normativity in an aprioristic manner; moral status is a function of data entities, not something solely human; agency is plural and thus is shared or sometimes delegated – in order to chart a proposal for (...) a posthuman bioethics. Posthuman is perhaps not the best expression available, but it covers the idea of a shift from a world centered on self-contained and exclusively human agency to a more comprehensive and relational way of thinking. The “posthuman” label should be understood as a rebuttal of biocentrism and anthropocentrism by moving closer to conceptions we encounter in population ethics or in discourse about biosocial and technical systems. Posthuman bioethics is “environmentalist” without losing the humanistic stance. The question regarding how suitable an infocentric bioethics is in practice remains to be settled. The moral principles in bioethics could be reconceived as relying on these new assumptions, in a postindividualistic manner that accepts formal primacy of causal digital artifacts in affording actions in a world of ambient algorithmic intelligence. (shrink)

Le terme big data désigne l'extraction, la manipulation et l'analyse des ensembles de données trop volumineux pour être traités de manière routinière. Pour cette raison, des logiciels spéciaux sont utilisés et, dans de nombreux cas, des ordinateurs et du matériel informatiques dédiés. Généralement, ces données sont analysées de manière statistique. Les données doivent être traitées avec des outils de collecte et d'analyse avancés, basés sur des algorithmes prédéterminés, afin d'obtenir des informations pertinentes. Les algorithmes doivent également prendre en compte les (...) aspects invisibles pour les perceptions directes. DOI: 10.13140/RG.2.2.19453.51684 . (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)

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)

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)

The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and on the free (...) movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. -/- ISBN 978-606-033-307-4 , DOI: 10.58679/MM14975 . (shrink)

Principalele probleme cu care se confruntă oamenii de știință în lucrul cu seturile mari de date (Big Data), evidențiind principale aspecte etice, luând în considerare inclusiv legislația din Uniunea Europeană. După o scurtă Introducere despre Big Data, secțiunea Tehnologia prezintă aplicațiile specifice în cercetare. Urmează o abordare a principalelor probleme filosofice specifice în Aspecte filosofice, și Aspecte legale cu evidențierea problemelor etice specifice din Regulamentul UE privind protecția datelor 2016/679 (General Data Protection Regulation, "GDPR"). Secțiunea Probleme etice detaliază aspectele specifice (...) generate de Big Data. După o scurtă secțiune Cercetarea Big Data, finalizez lucrarea cu prezentarea Concluziilor pentru etica cercetării în lucrul cu Big CUPRINS: Abstract 1. Introducere - 1.1 Definiții - 1.2 Dimensiunile Big Data 2. Tehnologia - 2.1 Aplicații - - 2.1.1 În cercetare 3. Aspecte filosofice 4 Aspecte legale - 4.1 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - Paradoxul (legal) al Big Data 5. Probleme etice - Etica în cercetare - Conștientizarea - Consimțământul - Controlul - Transparența - Încrederea - Proprietatea - Supravegherea și securitatea - Identitatea digitală - Realitatea ajustată - De-anonimizarea - Inegalitatea digitală - Confidențialitatea 6. Cercetarea Big Data Concluzii Bibliografie DOI: 10.13140/RG.2.2.27629.33761 . (shrink)

Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart. We also propose a new position tailor-made for coping with dark data and (...) general data management. We call it the scientific data officer and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns—and proposes solutions—to the control and authority that the SDO would acquire. (shrink)

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, (...) knowledge, and capacity, and reducing hazard. (shrink)

The importance of digital technologies for social and economic developments and a growing focus on data collection and privacy concerns have made the Internet a salient and visible issue in global politics. Recent developments have increased the awareness that the current approach of governments and business to the governance of the Internet and the adjacent technological spaces raises a host of ethical issues. The significance and challenges of the digital age have been further accentuated by a string of highly exposed (...) cases of surveillance and a growing concern about issues of privacy and the power of this new industry. This special issue explores what some have referred to as the “Internet-industrial complex”—the intersections between business, states, and other actors in the shaping, development, and governance of the Internet. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts (...) associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (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)

Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision (...) and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services. (shrink)