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)

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)

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)

The human environment is currently undergoing massive change amid the rapid adoption of information and communications technology (ICT). ICT can be characterized as offering an opportunity to consider the nature of humanity, create new values, and foster new cultures. As humans, the question that technical innovation relating to Artificial Intelligence (AI) and Robots thrusts before us is, “What is a human?” What exactly are the things that AI will never be able to do, no matter how close it gets to (...) human capabilities? What makes us humans, and what is it that only humans can do? This essay discusses the impact of AI technology on humans and society from the perspective of a turning point in how people perceive themselves and humanity. (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)

Les principaux problèmes rencontrés par les scientifiques qui travaillent avec des ensembles de données massives (mégadonnées, Big Data), en soulignant les principaux problèmes éthiques, tout en tenant compte de la législation de l'Union européenne. Après une brève Introduction au Big Data, la section Technologie présente les applications spécifiques de la recherche. Il suit une approche des principales questions philosophiques spécifiques dans Aspects philosophiques, et Aspects juridiques en soulignant les problèmes éthiques spécifiques du règlement de l'UE sur la protection des données (...) 2016/679 (General Data Protection Regulation, « GDPR »). La section Problèmes éthiques détaille les problèmes spécifiques générés par le big data. Après une brève section de Recherche de big data, sont présentées les Conclusions sur l’éthique de la recherche dans l’utilisation du big data. SOMMAIRE: Abstract 1. Introduction - 1.1 Définitions - 1.2 Les dimensions du big data 2. La technologie - 2.1 Applications - - 2.1.1 En recherche 3. Aspects philosophiques 4 Aspects juridiques - 4.1 RGPD (GDPR) - - Étapes du traitement des données personnelles - - Principes du traitement des données - - Politique de confidentialité et transparence - - Finalités du traitement des données - - Confidentialité par conception et confidentialité implicite - - Le paradoxe (juridique) des mégadonnées 5. Problèmes éthiques - L'éthique dans la recherche - Prise de conscience - Consentement - Contrôle - Transparence - Confiance - Propriété - Surveillance et sécurité - Identité numérique - Réalité ajustée - De-anonymisation - Inégalité numérique - Confidentialité 6. Recherche des mégadonnées Conclusions Bibliographie DOI: 10.13140/RG.2.2.10128.56328 . (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)

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)

Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components. DOI: 10.13140/RG.2.2.12784.00004 .

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)

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

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

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)

Policy and technological transformation have coalesced to usher in massive changes to educational systems over the past two decades. Teachers’ roles, subjectivities and professional identities have been subject to sweeping changes enabled by sophisticated forms of governance. Simultaneously, students have been recast as ‘learners’; like teachers, learners have become subject to new forms of governance, through technological surveillance and datafication. This paper focuses on the intersection of the metrics driven approach to education and the political as a way to re-think (...) the future of schooling in more explicitly philosophical terms. This exploration starts with a critical examination of constructions of teachers, learners and the digital data-driven educational culture in order to explicate the futures being generated. The trajectory of this future is explored through reference to the techno-educational models currently being developed in Silicon Valley. Drawing on Deleuze’s notion of control societies we contribute to the ongoing philosophical investigation of the datafication of education; a necessary discussion if we are to explore the future implications of schooling in a technologically saturated world. We present consideration of the past, present and future, as three ways of considering alternatives to a datafied education system. Alternative conceptualisations of the future of schooling are possible which offer ways of understanding and politicising what happens when we impose data-driven accountabilities into people’s lives. (shrink)

In this study, I examined 12 reports published by a neoliberal think tank proposing to reshape public education in New Zealand. In terms of the larger social processes and structures involved, the think tank’s self-declared positioning of this advocacy is that of a primary definer, ostensibly an expert voice, communicating through the media. My two research goals in this study were to identify the types of educational change being promoted and to uncover the discursive means employed. The sample of 12 (...) reports, published from 2013 to 2018, comprised 647 pages of text. I performed three types of analysis: thematic analysis to identify the ideology of the advocacy, genre analysis of their discursive resources and corpus analysis. The thematic findings reveal proposals to reshape public education through performativity, managerialism and marketization. The genre analysis found the reports followed the structure of the business case genre and drew on elements of business memos, data presentation, sound bite and opinion journalism. The corpus findings provide further linguistic evidence for the main themes. (shrink)

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

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)

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

We describe some of the ways that the field of content analysis is being transformed in an Era of Big Data. We argue that content analysis, from its beginning, has been concerned with extracting the main meanings of a text and mapping those meanings onto the space of a textual corpus. In contrast, we suggest that the emergence of new styles of text mining tools is creating an opportunity to develop a different kind of content analysis that we describe as (...) a computational hermeneutics. Here the goal is to go beyond a mapping of the main meaning of a text to mimic the kinds of questions and concerns that have traditionally been the focus of a hermeneutically grounded close reading, a reading that focuses on what Kenneth Burke described as the poetic meanings of a text. We illustrate this approach by referring to our own work concerning the rhetorical character of US National Security Strategy documents. (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)

Though full of promise, Big Data research success is often contingent on access to the newest, most advanced, and often expensive hardware systems and the expertise needed to build and implement such systems. As a result, the accessibility of the growing number of Big Data-capable technology solutions has often been the preserve of business analytics. Pay as you store/process services like Amazon Web Services have opened up possibilities for smaller scale Big Data projects. There is high demand for this type (...) of research in the digital humanities and digital sociology, for example. However, scholars are increasingly finding themselves at a disadvantage as available data sets of interest continue to grow in size and complexity. Without a large amount of funding or the ability to form interdisciplinary partnerships, only a select few find themselves in the position to successfully engage Big Data. This article identifies several notable and popular Big Data technologies typically implemented using large and extremely powerful cloud-based systems and investigates the feasibility and utility of development of Big Data analytics systems implemented using low-cost commodity hardware in basic and easily maintainable configurations for use within academic social research. Through our investigation and experimental case study, we found that not only are solutions like Cloudera’s Hadoop feasible, but that they can also enable robust, deep, and fruitful research outcomes in a variety of use-case scenarios across the disciplines. (shrink)

Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes. With this in mind, the Science and Values Working Group of the COST Action CHIP ME ‘Citizen's Health through (...) public-private Initiatives: Public health, Market and Ethical perspectives’ identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals’ trust in research. We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment. (shrink)

Precision Medicine has become a common label for data-intensive and patient-driven biomedical research. Its intended future is reflected in endeavours such as the Precision Medicine Initiative in the USA. This article addresses the question whether it is possible to discern a new ‘medical cosmology’ in Precision Medicine, a concept that was developed by Nicholas Jewson to describe comprehensive transformations involving various dimensions of biomedical knowledge and practice, such as vocabularies, the roles of patients and physicians and the conceptualisation of disease. (...) Subsequently, I will elaborate my assessment of the features of Precision Medicine with the help of Michel Foucault, by exploring how precision medicine involves a transformation along three axes: the axis of biomedical knowledge, of biomedical power and of the patient as a self. Patients are encouraged to become the managers of their own health status, while the medical domain is reframed as a data-sharing community, characterised by changing power relationships between providers and patients, producers and consumers. While the emerging Precision Medicine cosmology may surpass existing knowledge frameworks; it obscures previous traditions and reduces research-subjects to mere data. This in turn, means that the individual is both subjected to the neoliberal demand to share personal information, and at the same time has acquired the positive ‘right’ to become a member of the data-sharing community. The subject has to constantly negotiate the meaning of his or her data, which can either enable self-expression, or function as a commanding Superego. (shrink)

The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the (...) public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage. (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)

This article explores datafication as a speculative discourse that fundamentally and instrumentally misunderstands data, not as a representational system, but as an ontology. This analysis of datafication takes a semiotic and media-archaeological approach to datafication, understanding it as an imaginary media system, and the article looks to supplementary discourses in data visualization and big data to clarify and expand an understanding of datafication as a prescriptive and speculative idea. This critique is sharpened through the exploration of a detailed study of (...) the early visualizations and unpublished fictions of W. E. B. Du Bois, whose imagined technology of the “megascope” gives a remarkable blueprint for contemporary discourses in data visualization and data science. Du Bois’s liberatory perspective is contrasted to a more paranoid example coming from the self-documented delusional system of Daniel Paul Schreber, who provides a much different vision of a totalizing media system in the form of an Aufschreibesysteme, which seems to presage another side of datafication as a surveillance system. Taken together, datafication, the megascope, and the Aufschreibesysteme work as three speculative media that imagine themselves as different instances or visions of a totalizing media system, a notion that has become especially significant in the technological, cultural, and economic systems of the twenty-first century. (shrink)

This article offers a detailed examination of the content of predictive policing applications. Crime prediction machines are used by governments to shape the moral behavior of police. They serve not only to predict when and where crime is likely to occur, but also to regulate police work. They calculate equivalence ratios, distributing security across the territory based on multiple cost and social justice criteria. Tracing the origins of predictive policing in the Compstat system, this article studies the shift from machines (...) to explore intuitions to applications removing the reflexive dimension of proactivity, thus turning prediction into the medium for “dosage” metrics of police work quantities. Finally, the article discusses how, driven by a critical movement denouncing the discriminatory biases of predictive machines, developers seek to develop techniques to audit training dataset and ways to calculate the reasonable amount of stop and frisk over the population. (shrink)

Many actors mobilize the cognitive, legal and technical tool-box of data protection when they discuss and address controversial issues such as digital mass surveillance. Yet, critical approaches to the digital only barely explore the politics of data protection in relation to data-driven governance. Building on governmentality studies and Actor-Network-Theory, this article analyses the potential and limits of using data protection to critique the ‘digital age’. Using the conceptual tool of dispositifs, it sketches an analytics of data protection and the emergence (...) of its configuration as ‘data protection by design and by default’. This exploration reminds us that governing through data implies, first and foremost, governing digital data. (shrink)

While it is often claimed that the emerging digital governmentality functions as a new apparatus of surveillance, the aim of this paper is to characterise this regime in relation to Foucault’s disc...

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)

This article shows how activists in the open data movement re-articulate notions of democracy, participation, and journalism by applying practices and values from open source culture to the creation and use of data. Focusing on the Open Knowledge Foundation Germany and drawing from a combination of interviews and content analysis, it argues that this process leads activists to develop new rationalities around datafication that can support the agency of datafied publics. Three modulations of open source are identified: First, by regarding (...) data as a prerequisite for generating knowledge, activists transform the sharing of source code to include the sharing of raw data. Sharing raw data should break the interpretative monopoly of governments and would allow people to make their own interpretation of data about public issues. Second, activists connect this idea to an open and flexible form of representative democracy by applying the open source model of participation to political participation. Third, activists acknowledge that intermediaries are necessary to make raw data accessible to the public. This leads them to an interest in transforming journalism to become an intermediary in this sense. At the same time, they try to act as intermediaries themselves and develop civic technologies to put their ideas into practice. The article concludes with suggesting that the practices and ideas of open data activists are relevant because they illustrate the connection between datafication and open source culture and help to understand how datafication might support the agency of publics and actors outside big government and big business. (shrink)

New data-driven ideas of healthcare have increased pressures to reform existing data infrastructures. This article explores the role of data governing institutions during a reform of both secondary health data infrastructure and related legislation in Finland. The analysis elaborates on recent conceptual work on data journeys and data frictions, connecting them to institutional and regulatory issues. The study employs an interpretative approach, using interview and document data. The results show the stark contrast between the goals of open and Big Data (...) inspired reforms and the existing institutional realities. The multiple tensions that emerged during the process indicate how data frictions emanate to the institutional level, and how mundane data practices and institutional dynamics are intertwined. The article argues that in the Finnish case, public institutions acted as sage-guards of public interest, preventing more controversial parts from passing. Finally, it argues that initiating regulatory and infrastructural reforms simultaneously was beneficial for solving the tensions of the initiative and analysing either side separately would have produced misleading accounts of the overall initiative. The results highlight the benefits of analysing institutional dynamics and data practices as connected issues. (shrink)

Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well as emergent forms of regulation that seek to set demands for corporate conduct and the protection of civic rights. And the Protective measures include methods of traceability and assessments of ‘good’ and ‘bad’ datasets and algorithms that are considered to be traceable, stable and contained. However, (...) these ways of thinking about both technology and ethics obscure a fundamental issue, namely that machine learning systems entangle data, algorithms and more-than-human environments in ways that challenge a well-defined separation. This article investigates the fundamental fallacy of most data reuse strategies as well as their regulation and mitigation strategies that data can somehow be followed, contained and controlled in machine learning processes. Instead, the article argues that we need to understand the reuse of data as an inherently entangled phenomenon. To examine this tension between the discursive regimes and the realities of data reuse, we advance the notion of reuse entanglements as an analytical lens. The main contribution of the article is the conceptualization of reuse that places entanglements at its core and the articulation of its relevance using empirical illustrations. This is important, we argue, for our understanding of the nature of data and algorithms, for the practical uses of data and algorithms and our attitudes regarding ethics, responsibility and regulation. (shrink)

Medio siglo después de que los ordenadores hayan entrado en nuestras vidas, los datos han empezado a eclosionar hasta tal punto que se puede afirmar que algo nuevo y especial está teniendo lugar. Cuando comienza la era del Big Data, las instituciones y organizaciones empresariales pasan a ser conscientes del alto potencial remanente en los datos que están almacenados en sus archivos. Así, los datos almacenados pasan a ser un activo de la organización y se utilizan para buscar valor en (...) ellos que conduzca hacia una toma de decisiones basada en datos. Sectores como servicios, sanidad, educación o la pro-pia área gubernamental están aplicando estos nuevos métodos de análisis de cara a inferir conocimiento mediante la aplicación del razonamiento deductivo. Pero la transformación de los datos en conocimiento no es una tarea fácil ni directa ya que los conjuntos de datos disponibles se encuentran generalmente desorganizados. Es la era del Big Data que emerge de la eclosión de los datos. (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)

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)

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)

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)

Organized immaturity refers to the capacity of widely institutionalized sociotechnical systems to challenge qualities of human enlightenment, autonomy, and self-determination. In the context of surveillance capitalism, where these qualities are continuously put at risk, data transparency is increasingly proposed as a means of restoring human maturity by allowing individuals insight and choice vis-à-vis corporate data processing. In this article, however, I draw on research on General Data Protection Regulation–mandated data transparency practices to argue that transparency—while potentially fostering maturity—itself risks producing (...) new forms of organized immaturity by facilitating user ignorance, manipulation, and loss of control of personal data. Considering data transparency’s relative “successes” and “failures” regarding the cultivation of maturity, I outline a set of possible remedies while arguing for a general need to develop more sophisticated ethical appreciations of transparency’s complex and potentially problematic implications for organized (im)maturity in the digital age. (shrink)

Living in an era of technological innovations, we must understand and trust them to benefit from these new technologies. In this context, paternalism is renewed as the so-called “soft-” or “Libertarian paternalism”. How can we face it and ensure freedom in the vortex of wellmeaning advice and persuasion? This paper will discuss 1. the characteristics of freedom since the 18th century from the perspective of the Enlightenment discourse in Germany by Mendelssohn and Kant, 2. the conditions for freedom in the (...) contemporary age of accelerating technological innovation, and 3. from trans-modernism, rethinking the dichotomy between otherness and autonomy, coercion and freedom, by the idea of 融通無碍 Yuzu-Muge, tactfulness in 華厳学 Hua-Yan Philosophy. (shrink)

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in (...) making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research. (shrink)