RESUMEN: Una crisis como la del coronavirus, que ha tenido lugar en un entorno digitalizado, ha puesto a prueba la capacidad y los límites del big data para proporcionarnos una imagen completa de la realidad e indicaciones para gestionar la pandemia. Uno de los aprendizajes que hemos de realizar es entender que los datos reflejan la desigualdad existente y, sin una correcta interpretación, invisibilizan a los grupos más vulnerables. Para ello lo más importante es dejar de concebir la realidad social (...) a partir de los criterios estandarizados y pensarla como compuesta de individuos y grupos. (shrink)

Resumen: El rápido avance de la digitalización y la hiperconectividad de las sociedades modernas en los últimos años ha dado lugar a la dataficación de la vida de las personas y a la revolución del big data. Estos dos fenómenos presentan un gran potencial que puede originar múltiples beneficios en multitud de aspectos de la vida de los ciudadanos, pero también hay que tener en cuenta las implicaciones y los peligros de estos. Este artículo se centra en los peligros provocados (...) por las grandes corporaciones tecnológicas del planeta, las denominadas GAFAM (Google, Amazon, Facebook, Apple y Microsoft). Los procesos de extracción, almacenamiento y análisis de datos de la vida de las personas llevados a cabo por las GAFAM presentan diversas implicaciones peligrosas para los propios ciudadanos entre las que destacan la monopolización de sus datos y la perdida de privacidad. En este artículo se profundizará en estas dos amenazas. Utilizando una metodología hermenéutico-crítica basada en el análisis de fuentes bibliográficas, el objetivo de este artículo es examinar los principales riesgos de las GAFAM para la ciudadanía y proponer soluciones a estos. Abstract: The rapid advance of digitization and hyperconnectivity of modern societies has led to the datafication of people's lives and the big data revolution. These two phenomena have great potential that can cause multiple benefits in many aspects of citizens' lives, but their implications and dangers must also be considered. This article focuses on the dangers caused by the large technology corporations on the planet, the so-called GAFAM (Google, Amazon, Facebook, Apple, and Microsoft). The processes of extraction, storage, and analysis of data from people's lives carried out by the GAFAM have various dangerous implications for the citizens themselves, among which stand out the monopolization of their data and the loss of privacy. This article will delve into these two threats. Using a hermeneutical-critical methodology based on the analysis of bibliographic sources, the objective of this article is to examine the main risks of GAFAM for citizens and propose solutions to them. (shrink)

Resumen: El presente artículo problematiza uno de los conceptos protagónicos con que se define nuestra contemporaneidad: la post-verdad. En la era de la información, la digitalización y el Big Data, el conocimiento objetivo parece ceder terreno ante la proliferación de discursos que niegan y distorsionan la realidad. Las fake news y el negacionismo climático se constituyen en los ejemplos paradigmáticos con que la digitalización contemporánea parece ir más allá de la verdad y de los estándares tradicionales de objetividad. Sin embargo, (...) ¿se trata realmente de una era en que prima la falsedad, la pura apariencia y la emocionalidad, o asistimos más bien a la emergencia de un nuevo modo de veridicción que instaura el advenimiento de lo digital? Basándonos en los planteamientos teóricos de Foucault y Latour, sostendremos que la digitalización contemporánea no debe ser aprehendida como una máquina generadora de post-verdad, sino como un modo de existencia que pone en juego un régimen de veridicción en que prima lo discontinuo, lo acelerante y lo asignificante. Abstract: This article analyses one of the main concepts that define our contemporary age: post-truth. In the time of information technology, digitization, and Big Data, objective knowledge seems to recede in front of the proliferation of discourses that deny and distort reality. Fake news and climate denial constitute paradigmatic examples that go beyond the truth and traditional standards of objectivity. However, is it really an era in which falsehood, pure appearance, and emotionality prevail, or is it the time for the emergence of a new mode of veridiction that establishes the digital becoming? Based on the theoretical approaches of Foucault and Latour, we will argue that contemporary digitization should not be apprehended as a machine that generates post-truth but instead as a mode of existence that brings into play a regime of veridiction in which discontinuity, acceleration, and asignification prevail and arise. (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)

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

Refining big data is a new multipurpose way to find, collect, and analyze information obtained from the web and off-line information sources about any research subject. It gives the opportunity to investigate (with an assumed level of statistical significance) the past and current status of information on a subject, and it can even predict the future. The refining of big data makes it possible to quantitatively investigate a wide spectrum of raw information on significant human issues—social, scientific, political, business, and (...) others. Refining creates a space for new, rich sources of information and opens innovative ways for research. The article describes a procedure for refining big data and gives examples of its use. (shrink)

Big data increasingly enables prediction of the behaviour and characteristics of individuals. This is ethically concerning on privacy grounds. However, this article discusses other reasons for concern. These predictions usually rely on generalisations about what certain sorts of people tend to do. Generalisations of this sort are often under scrutiny in legal cases, where, for example, lawyers argue that people with prior convictions are more likely to be guilty of the crime they are currently on trial for. This article applies (...) criteria for distinguishing acceptable from unacceptable generalisations in legal cases to a number of big data examples. It argues that these criteria are helpful, and highlights three issues that should be taken into account when deciding whether predictions about individuals are ethical. (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)

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)

The question we pose in this paper is: How can wisdom and its inherent drive for integration help information systems in the development of practices for responsibly and ethically managing and using big data, ubiquitous information and algorithmic knowledge and so make the world a better place? We use the recent financial crises to illustrate the perils of an overreliance on and misuse of data, information and predictive knowledge when global Information Systems are not wisely integrated. Our analysis shows that (...) the global financial crisis was in part caused by a serious lack of integration of information with the larger context of social, cultural, economic and political dynamics. Integration of all the variables in a global and information hungry industry is exceptionally difficult, and so “exceptionality” of some kind is needed to make sufficient integration happen. Wisdom, we suggest, is the exceptionality needed to lead successful integration. We expect that a wisdom-based shift can lead to more organizationally effective and socially responsible Information Systems. (shrink)

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede (...) informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. (shrink)

Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, especially in terms (...) of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. (shrink)

ZusammenfassungDer vorliegende Artikel widmet sich der Frage, wie Spiele und spielen zur Big-Data-basierten Wissensproduktion in der Hochenergiephysik beitragen. Als Beispiel dienen Detektorkollaborationen am Large Hadron Collider (LHC) der Europäischen Organisation für Kernforschung (CERN), in denen die Autorin seit 2014 kulturanthropologische Feldforschung unternommen hat. Der ludische Aspekt der Wissensproduktion wird hier in drei verschiedenen Dimensionen analysiert: der symbolischen, der ontologischen und der epistemischen. Erstere verweist auf das CERN als Ort, an dem ein kosmologisches Wahrscheinlichkeitsspiel mithilfe von Monte-Carlo-Simulationen durchgeführt wird. Die Zweite (...) wird durch kompetitive Infrastrukturen in der Arbeitspraxis verschiedener Kollaborationsgruppen sichtbar. Die dritte Dimension entfaltet sich durch ludische Plattformen, wie etwa Online Challenges und Citizen Science Games, die zur Entwicklung von machine learning algorithmen beitragen, deren Funktion notwendig ist, um die ungeheuren Mengen an Big Data verarbeiten zu können, die durch die Experimente gesammelt werden. Das CERN wird als Ort des Deep Play beschrieben, einem von Clifford Geertz entwickeltem Konzept, das dazu beiträgt, größere soziale und kulturelle Ordnungen durch die Analyse ludischer Kollektivphänomene zu begreifen. Der Artikel setzt sich darüber hinaus mit Peter Galisons Idee der Trading Zone auseinander und schlägt vor, diese in Zeiten von Big Data als Playground (Spielplatz) zu begreifen. Die Autorin hofft damit zu einer breiteren sozio-historiografischen Diskussion in den Science and Technology Studies (STS, Wissenschafts- und Technikforschung) und nicht zuletzt der Kulturanthropologie beizutragen, indem das Ludische in den Wissenschaften als zentrales Element für das Verständnis kollaborativer Wissensproduktion begriffen wird. (shrink)

Resumen: El presente artículo ofrece un estado del arte de cómo se ha venido a estudiar empíricamente la relación entre política y redes sociales en la última década, desde el punto de vista de la naturaleza del objeto de estudio, las nuevas técnicas de análisis y métodos sobre las que se han apoyado las ciencias sociales, las agendas de investigación a que ha dado lugar y algunos de los dilemas éticos que suscita. El artículo consta de tres partes. Primero, desarrollamos (...) una discusión de los principales métodos de análisis de la relación entre política y redes sociales, el big data y los algoritmos, y lo comparamos con los métodos tradicionales de encuestas de opinión. Segundo, ofrecemos una revisión de las principales preocupaciones que han guiado la investigación en los últimos diez años: la representación y campañas políticas, nuevas comunidades políticas y nuevas formas de acción colectiva. Finalmente, la tercera sección reflexiona sobre los dilemas éticos y debates que plantea el uso de las nuevas técnicas de análisis en las ciencias sociales.: The article offers a state of art about the empirical study of the relationship between politics and social media during the last decade, from the point of view of the nature of the subject, new research techniques and methods used in the social sciences, the research agendas it has fostered, and some of the ethical dilemmas this research raises. The article is divided in three main parts. Firstly, we develop a discussion of the main research methods to approach the relationship between politics and digital media, big data and algorithms, which we compare with traditional opinion polls. Secondly, we offer a review of the main issues that have guided this research agenda during the last ten years: political representation and campaigns, emerging political communities, and new forms of collective action. Finally, the last section reflects on the ethical dilemmas and debates that arise from the utilization of new research methods in the social sciences. (shrink)

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

The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications (...) of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

En las actuales sociedades avanzadas cada ciudadano genera a diario una ingente cantidad de datos sobre su persona. La masiva utilización de esta información por las tecnologías Big Data suscita profundos y justificados recelos en muchas personas. Sobre todo por la incidencia que podría tener en derechos humanos como la privacidad, la libertad, la igualdad y la justicia social y porque el formidable poder que representa BD es controlado por un número cada vez más anónimo y reducido de personas. La (...) utilización de herramientas tecnológicas en todos los niveles del aprendizaje podría reportar grandes ventajas, a pesar de los recelos que suscita en algunas personas. En sí mismos, los conoci-mientos no tienen connotaciones éticas, sino que su moralidad radica en el uso que de ellos se haga. Los ángeles y los demonios no están en la técnica, sino en el corazón de quienes se sirven de ella. Será muy difícil enfrentarse a problemas como los arriba mencionados, si los principios morales de que partimos en nuestros debates no son distintos de los habituale. (shrink)

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports (...) big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions. (shrink)

This commentary recaps a Centre for Central Banking Studies event held at the Bank of England on 2–3 July 2014. The article covers three main points. First, it situates the Centre for Central Banking Studies event within the context of the Bank’s Strategic Plan and initiatives. Second, it summarises and reflects on major themes from the event. Third, the article links central banks’ emerging interest in Big Data approaches with their broader uptake by other economic agents.

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

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)

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of this "massive (...) data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature. Defines Big Data applications in health Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data Provides the recommendations and steps necessary for successful management and governance of personal health data Helps readers determine what conditions are essential for the development of the study of Big Data. (shrink)

ZusammenfassungGenomdaten, Kernstück der 2008 ausgerufenen Big Data-Revolution der Biologie, werden voll automatisiert sequenziert und analysiert. Der Wechsel von der manuellen Laborpraktik der Elektrophorese-Sequenzierung zu DNA-Sequenziermaschinen und softwarebasierten Analyseprogrammen vollzog sich zwischen 1982 und 1992. Erst dieser Wechsel ermöglichte die Flut an Daten, die mit der zweiten und dritten Generation der DNA-Sequenzierer erheblich zunimmt. Doch mit diesem Wechsel verändern sich auch die Validierungsstrategien der Genomdaten. Der Beitrag untersucht beides – die Automatisierung und die damit verbundene Validierungskultur – um ein Bild der (...) Komplexität der Datengenerierung und deren Datenpositivismus zu geben. Leitend ist dabei die Frage, ob dieser Datenpositivismus die Grundlage der aktuell angekündigten Big Data-Revolution der Molekularbiologie ist oder deren Datenhybris. (shrink)

Today’s economy has transitioned from the traditional brick and mortar structure of doing business to that of digitalized economy. The latter functions with the aid of technological tools with ‘data’ being the most significant tool in today’s context. The issue has become even more critical with the advent of ‘big data’. It is argued that accumulation, analysis and usage of ‘big data’ enable creation of varied forms of entry barriers for new entrants and information asymmetries for customers which in turn (...) affect ‘market competition’ adversely (Santesteban & Longpre The Antitrust Bulletin,65(3), 459-485, 2020; Fast et al., 2021). Consequently, it is pertinent to revisit the traditional understanding of ‘market competition’ as stipulated in the antitrust laws across jurisdictions and specifically the Indian Competition Act, 2002. The existing regime being centred around neo-classical price theory is not equipped to comprehend the various dimensions of the digitalised world where ‘data’ is the new form of currency having effects on market power and, consequently, on market competition. Digital markets are multi-sided and non-linear in nature where ‘big data’ acts as a lubricant for their smooth functioning. Whether access to data provides any form of competitive advantage is the central question. A resource to provide any form of competitive advantage must be distinct, rare, inimitable, non-substitutable and valuable (Barney, 1991). If data carries such characteristics, the continuous percolation and usage of technology powered by ‘big data’ into the markets will affect the understanding of the basic competitive wrongs. Potential interdependencies between technology, data and market power also affect the understanding of ‘dominance’ as stipulated under the existing law. The unique challenges which ‘big data’ poses are not only restricted to competition policy but also extended to enforcement as they trigger overlapping constitutional concerns in relation to privacy. This demands for looking at the existing gap between competition law/policy and the ‘data’-driven challenges being faced in this ever-evolving digital economy. (shrink)

Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either limitedly or not (...) at all, and their prospects of doing so in the future are limited too. (shrink)

The technological possibilities for new data sources in media psychology, such as online live recordings, called Live Streaming, are growing continuously. These sources do not only offer plentiful quantitative material but also a fairly new access to ecologically valid and unobtrusive observation of problem-solving and decision-making processes. However, to exploit these potentials, epistemological and methodological reflection should guide research. The availability of Big Data and naturally occurring data sets allows to revise the historical controversies on the eligibility of self-description. Drawing (...) on such reflections, media psychology can contribute to renovate well established research methods, such as think aloud protocols, in order to enhance their empirical claims. Apart from other confident attempts to improve these methods, phenomenology and ethnomethodology offer a fruitful account to develop innovative data sources for self-description. Yet, this approach does not support a recurrence of self-description’s previous application but proposes an epistemological shift towards more subtle observations. In order to convey the potentials of media psychology, the risk of repeating classical mistakes, such as introspectionism, have to be regarded. Beyond these fallacies, however, modern digital technology holds encouraging potentials which have already partly been sighted by video gaming research. Due to the similarity of digital environments to laboratory setups, there remains to be a continuity from offline to online research, from traditional data to Big Data. Nevertheless, a true advance into new possibilities requires understanding the qualitative meaning of such data. (shrink)

Este artículo se centra en el análisis del uso de la industria del big data en la política. Se examina de forma pormenorizada el caso de la empresa Cambridge Analytica y se profundiza en los efectos del uso de la tecnología del big data en el referéndum de permanencia de Reino Unido en la Unión Europea y en las elecciones presidenciales estadounidenses de 2016. El objetivo es exponer los efectos nocivos que tiene el uso de la tecnología del big data (...) en los procesos electorales y los daños que esta tecnología puede producir en los sistemas democráticos. (shrink)

The purpose of this paper is to explore the epistemological tensions embedded within big data and data-driven technologies to advance a socio-political reconsideration of the public dimension in the assessment of their implementation.,This paper builds upon (and revisits) the European Union’s (EU) normative understanding of artificial intelligence (AI) and data-driven technologies, blending reflections rooted in philosophy of technology with issues of democratic participation in tech-related matters.,This paper proposes the conceptual design of sectorial and/or local-level e-participation platforms to ignite an ongoing (...) discussion – involving experts, private actors, as well as cognizant citizens – over the implementation of data-driven technologies, to avoid siloed, tech-solutionist decisions.,This paper inscribes the EU’s normative approach to AI and data-driven technologies, as well as critical work on the governance of these technologies, into a broader political dimension, suggesting a way to democratically and epistocratically opening up the decisional processes over the development and implementation of these technologies and turn such processes into a systemic civic involvement. (shrink)

Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if (...) combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research. (shrink)

In Homo Deus, Yuval Noah Harari argues that technological advances of the twenty-first century will usher in a significant shift in how humans make important life decisions. Instead of turning to the Bible or the Quran, to the heart or to our therapists, parents, and mentors, people will turn to Big Data recommendation algorithms to make these choices for them. Much as we rely on Spotify to recommend music to us, we will soon rely on algorithms to decide our careers, (...) spouses, and commitments. Harari also predicts that next, the state will take away individuals’ rights to make their own choices about their lives. If Google knows where your children would flourish best in school, why should the state allow a fallible human parent to decide? Liberalism—which, as Harari uses this term, refers to a state of society in which human freedom to choose is respected and championed—will collapse. In this paper, I argue that Harari’s conception of the future implications of recommendation algorithms is deeply flawed, for two reasons. First, users will not rely on algorithms to make decisions for them because they have no reason to trust algorithms, which are developed by companies with their own incentives, such as profit. Second, for most of our life decisions, algorithms will not be able to be developed, because the factors relevant to the decisions we face are unique to our situation. I present an alternative depiction of the future: instead of relying on algorithms to make decisions for us, humans will use algorithms to enhance our decision-making by helping us consider the most relevant choices first and notice information we might not otherwise. Finally, I will also argue that even if computers could make many of our decisions for us, liberalism as a political system would emerge unscathed. (shrink)

The arrival of “big data” promises new degrees of precision in understanding human behavior. Could it also allow drawing a finer line between “choice” and “circumstances”? In a culture in which individual responsibility continues to be celebrated, this raises questions about new opportunities for institutional design with a stronger focus on individual responsibility. But what is it that can be learned from big data? In this paper I argue that we should not expect a “god’s eye view” on choice versus (...) circumstances from big data. “Responsibility” is a social construct that depends on the logic of different social situations, as well as our epistemic access to certain counterfactuals (e.g., whether an agent “could have acted differently”). It is this epistemic dimension that changes with the arrival of big data. But while this might help overcome some epistemic barriers, it might also create new problems, e.g., because of polluted and hence biased data. This is not just a theoretical problem; it is directly connected to the regulation of insurance. The new developments force us to directly confront questions about mutualist versus solidaristic forms of insurance, and more generally about how much weight to ascribe to individual responsibility, given all we know about unequal background circumstances. (shrink)

In the paper, several data reduction techniques for machine learning from big datasets are discussed and evaluated. The discussed approach focuses on combining several techniques including stacking, rotation, and data reduction aimed at improving the performance of the machine classification. Stacking is seen as the technique allowing to take advantage of the multiple classification models. The rotation-based techniques are used to increase the heterogeneity of the stacking ensembles. Data reduction makes it possible to classify instances belonging to big datasets. We (...) propose to use an agent-based population learning algorithm for data reduction in the feature and instance dimensions. For diversification of the classifier ensembles within the rotation also, alternatively, principal component analysis and independent component analysis are used. The research question addressed in the paper is formulated as follows: does the performance of a classifier using the reduced dataset be improved by integrating the data reduction mechanism with the rotation-based technique and the stacking? (shrink)

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine algorithm based on travel consumer sentiment analysis and builds an Hadoop (...) Distributed File System system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification. (shrink)

Aiming at the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, an English teaching ability evaluation algorithm based on big data fuzzy K-means clustering and information fusion is proposed. Firstly, the author uses the idea of K-means clustering to analyze the collected original error data, such as teacher level, teaching facility investment, and policy relevance level, removes the data that the algorithm considers unreliable, uses the remaining valid data to calculate the weighting factor (...) of the modified fuzzy logic algorithm, and evaluates the weighted average with the node measurement data and gets the final fusion value. Secondly, the author integrates the big data information fusion and K-means clustering algorithm, realizes the clustering and integration of the index parameters of English teaching ability, compiles the corresponding English teaching resource allocation plan, and realizes the evaluation of English teaching ability. Finally, the results show that using this method to evaluate English teaching ability has better information fusion analysis ability, which improves the accuracy of teaching ability evaluation and the efficiency of teaching resources application. (shrink)

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

ABSTRACT:Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price. This can generate some interesting benefits, such as a better state of affairs in terms of equality of both welfare and resources, as well as increased social welfare. However, these benefits are countered by considerations of relational equality. This article takes up the market-failures approach as its basis to demonstrate what is wrong with using (...) big data to personalize prices. The article offers an improvement to the market-failures approach and argues that what is wrong with using big data to personalize prices is that it unfairly undermines consumers’ ability to benefit from the market, which is the very point of having a market. (shrink)

Social scientists and data analysts are increasingly making use of Big Data in their analyses. These data sets are often “found data” arising from purely observational sources rather than data derived under strict rules of a statistically designed experiment. However, since these large data sets easily meet the sample size requirements of most statistical procedures, they give analysts a false sense of security as they proceed to focus on employing traditional statistical methods. We explain how most analyses performed on Big (...) Data today lead to “precisely inaccurate” results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size. Before any analyses are performed on large data sets, we recommend employing a simple data segmentation technique to control for some major components of observational data biases. These segments will help to improve the accuracy of the results. (shrink)

Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed (...) data raises unique questions regarding the interplay of multiple regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle. (shrink)

The contribution of Big Data to social science is not limited to data availability but includes the introduction of analytical approaches that have been developed in computer science, and in particular in machine learning. This brings about a new ‘culture’ of statistical modelling that bears considerable potential for the social scientist. This argument is illustrated with a brief discussion of model-based recursive partitioning which can bridge the theory and data-driven approach. Such a method is an example of how this new (...) approach can help revise models that work for the full dataset: it can be used for evaluating different models, a traditional weakness of the ‘traditional’ statistical approach used in social science. (shrink)

As big data techniques become widespread in journalism, both as the subject of reporting and as newsgathering tools, the ethics of data science must inform and be informed by media ethics. This article explores emerging problems in ethical research using big data techniques. It does so using the duty-based framework advanced by W.D. Ross, who has significantly influenced both research science and media ethics. A successful framework must provide stability and flexibility. Without stability, ethical precommitments will vanish as technology rapidly (...) shifts costs. Without flexibility, traditional approaches will rapidly become obsolete in the face of technological change. The article concludes that Ross's duty-based approach both provides stability in the face of rapid technological change and flexibility to innovate to achieve the original purpose of basic ethical principles. (shrink)

The question we pose in this paper is: How can wisdom and its inherent drive for integration help information systems in the development of practices for responsibly and ethically managing and using big data, ubiquitous information and algorithmic knowledge and so make the world a better place? We use the recent financial crises to illustrate the perils of an overreliance on and misuse of data, information and predictive knowledge when global Information Systems are not wisely integrated. Our analysis shows that (...) the global financial crisis was in part caused by a serious lack of integration of information with the larger context of social, cultural, economic and political dynamics. Integration of all the variables in a global and information hungry industry is exceptionally difficult, and so “exceptionality” of some kind is needed to make sufficient integration happen. Wisdom, we suggest, is the exceptionality needed to lead successful integration. We expect that a wisdom-based shift can lead to more organizationally effective and socially responsible Information Systems. (shrink)

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated (...) in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it. (shrink)

Big data, big impact -- Values and actions -- Current practices -- Aligning values and actions.

Agricultural Big Data analytics (ABDA) is being proposed to ensure better farming practices, decision-making, and a sustainable future for humankind. However, the use and adoption of these technologies may bring about potentially undesirable consequences, such as exercises of power. This paper will analyse Brey’s five distinctions of power relationships (manipulative, seductive, leadership, coercive, and forceful power) and apply them to the use agricultural Big Data. It will be shown that ABDA can be used as a form of manipulative power to (...) initiate cheap land grabs and acquisitions. Seductive power can be exercised by pressuring farmers into situations they would not have otherwise chosen (such as installing monitors around their farm and limited access to their farm and machinery). It will be shown that agricultural technology providers (ATPs) demonstrate leadership power by getting farmers to agree to use ABDA without informed consent. Coercive power is exercised when ATPs threaten farmers with the loss of ABDA if they do not abide by the policies and requirements of the ATP or are coerced to remain with the ATP because of fear of legal and economic reprisal. ATPs may use ABDA to determine willingness-to-pay rates from farmers, using this information to force farmers into precarious and vulnerable positions. Altogether, this paper will apply these five types of power to the use and implementation of ABDA to demonstrate that it is being used to exercise power in the agricultural industry. (shrink)

Partiendo del intenso impacto que está teniendo el big data y el resto de tecnologías conexas en nuestra existencia personal y social, el presente artículo busca, en primer lugar, realizar una aproximación a la naturaleza del conocimiento que los datos masivos generan desde el concepto foucaultiano de saber-poder, como también desentrañar el modo en que dicho conocimiento es utilizado para legitimar decisiones que se toman en la incertidumbre. En segundo lugar, intentará analizar qué novedad supone dicho saber en relación a (...) su más inmediato precedente: la estadística y, por último, tendrá como fin examinar el tipo de poder que surge de dicho saber, en relación especialmente con las ideas, de nuevo en la estela de Michel Foucault, acerca del poder pastoral y la conducción a través de la verdad confesada. (shrink)

Introduction The paradigm shift to a knowledge‐based economy has incremented the use of personal information applied to health‐related activities, such as biomedical research, innovation, and commercial initiatives. The convergence of science, technology, communication and data technologies has given rise to the application of big data to health; for example through eHealth, human databases and biobanks. Methods In light of these changes, we enquire about the value of personal data and its appropriate use. In order to illustrate the complex ground on (...) which big data applied to health develops, we analyse the current situation of the European Union and two cases: the Catalan VISC+/PADRIS and the UK Biobank, as perspectives. Discussion and conclusions Personal health‐related data in the context of the European Union is being increasingly used for big data projects under diverse schemes. There, public and private sectors participate distinctively or jointly, pursuing very different goals which may conflict with individual rights, notably privacy. Given that, this paper advocates for stopping the unjustified accumulation and commercialisation of personal data, protecting the interests of citizens and building appropriate frameworks to govern big data projects for health. A core tool for achieving such goals is to develop consent mechanisms which allow truly informed but adaptable consent, conjugated with the engagement of donors, participants and society. (shrink)

Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds that (...) the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context. (shrink)

This paper investigates epistemological and ethical implications of the growingavailability of direct-to-consumer genetic testing for the science and society. Direct-toconsumer genetic testing is characterized as the genetic testing sold directly to consumerswithout any assistance from professionals. By offering empowerment and control, companiesconvince consumers to sequence their genome by granting the company access to theirgenetic data in exchange to results that are not always accurate. To which extent doconsumers properly understand the results of their genetic testing? Are consumers aware ofthe possible (...) implications for disclosing genetic information to private companies? I addressthese questions in two steps: firstly, I discuss epistemological implications of thedevelopment of Genomics, understood as a data-intensive science, by delving into thetheoretical commitments of the concept of gene and the notion of circular causality.Secondly, I reflect upon the privacy risks of taking direct-to-consumer genetic testing in adata-rich world. Finally, I draw some conclusions on the possible consequences of direct-toconsumer genetic testing by suggesting key-concepts that may help to clarify the limits andscope of genetic testing. (shrink)

Unter der Perspektive von Big Data werden ethische Grundfragen fokussiert, die sich aus der Entwicklung und Anwendung fortgeschrittener Informationstechnologien ergeben. Allen fortgeschrittenen informatischen Werkzeugen ist gemeinsam, dass sie mit großen Datenmengen umgehen müssen, diese zu analysieren, zu verarbeiten und möglichst selbständig auf die Analysate regulierend und steuernd zu reagieren haben. Im Fokus steht erstens die Anschließbarkeit eines speziellen Diskurses über fortgeschrittene Informationstechnologien innerhalb der Technikethik, zweitens die Verortbarkeit spezifischer Handlungsprobleme in Prozessen, die die gesamte Gesellschaft und den ethischen Status des (...) Individuums betreffen, und drittens die Aufweisung metaphysischer Voraussetzungen gegenwärtiger Einstellungen im Umgang mit Analysaten, die aus großen, insbesondere fließenden Datenmengen gewonnen werden. Es wird versucht die Kontinuität bestimmter ethischer Grundkonflikte im informationstechnologischen Diskurs aufzuweisen. Schlüsselthemen sind u.a. die Dialektik von Ermächtigung und Entmündigung im Umgang mit modernen Informationstechnologien sowie, damit verbunden, der Verlust von Widerständigkeitserfahrungen, der mit einem Wirklichkeitsverlust einhergeht. Die Studie versteht sich als Orientierungshilfe und als Beitrag zur philosophischen Verortung und Einschätzung einer Bereichsethik. Der Autor Prof. Dr. Klaus Wiegerling arbeitete bis zur Pensionierung am Institut für Technikfolgenabschätzung und Systemanalyse (ITAS) des KIT Karlsruhe. Er ist Lehrbeauftragter an verschiedenen deutschen und ausländischen Universitäten. (shrink)