Showing papers in "Big Data & Society in 2019"
TL;DR: In this paper, the authors analyse data as a form of capital that is distinct from, but complementary to, human capital and argue that the collection and circulation of data is now a central element of increasingly more sectors of contemporary capitalism.
Abstract: The collection and circulation of data is now a central element of increasingly more sectors of contemporary capitalism. This article analyses data as a form of capital that is distinct from, but h...
372 citations
TL;DR: It is proposed to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications.
Abstract: Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.
164 citations
TL;DR: In this article, the role of digital technology in food production has been discussed in the context of precision agriculture, a set of claims about the importance of digital technologies in the field of food production.
Abstract: Recent technological and methodological changes in farming have led to an emerging set of claims about the role of digital technology in food production. Known as precision agriculture, the integra...
95 citations
TL;DR: The full text of the news articles is made available, together with veracity labels previously assigned based on manual assessment of the articles’ truth content, for building a system to automatically detect misinformation in news.
Abstract: Fake news has become an important topic of research in a variety of disciplines including linguistics and computer science. In this paper, we explain how the problem is approached from the perspect...
83 citations
TL;DR: In this article, the paradigm shift of data-fication from the perspective of civil society is discussed, looking at how individuals and groups engage with datafication, it complements the notion of "data...
Abstract: This article approaches the paradigm shift of datafication from the perspective of civil society. Looking at how individuals and groups engage with datafication, it complements the notion of “data ...
68 citations
TL;DR: This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes and suggests a framework for studying such classification closely tied to different steps in the work process.
Abstract: Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work wel...
62 citations
TL;DR: Data activism, promoting new forms of civic and political engagement, has emerged as a response to problematic aspects of datafication that include tensions between data openness and data ownership.
Abstract: Data activism, promoting new forms of civic and political engagement, has emerged as a response to problematic aspects of datafication that include tensions between data openness and data ownership...
61 citations
TL;DR: Concerns are raised with the argument that because digital data and information can be in more than one place at once, governance models for physical common-pool resources cannot be easily transposed to digital commons.
Abstract: In recent years, critical scholarship has drawn attention to increasing power differentials between corporations that use data and people whose data is used. A growing number of scholars see digita...
60 citations
TL;DR: The aim of this approach is to broaden the common analytical focus on algorithms as biased and opaque black boxes, and to instead highlight the many relations that algorithms are interwoven with, to highlight how algorithms fold heterogeneous things.
Abstract: This article proposes an analytical approach to algorithms that stresses operations of folding. The aim of this approach is to broaden the common analytical focus on algorithms as biased and opaque ...
44 citations
TL;DR: It is argued that in order to harness anti-discrimination regulation, it needs to confront emergent forms of discrimination or risk creating new invisibilities, including invisibility from existing safeguards, via intersectional and post-colonial analysis.
Abstract: The potential for biases being built into algorithms has been known for some time (e.g., Friedman and Nissenbaum, 1996), yet literature has only recently demonstrated the ways algorithmic profiling...
38 citations
TL;DR: 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.
Abstract: 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 no...
TL;DR: In this paper, the authors focus on the increasing datafication and digitalisation of governance, and propose Critical Data Studies (CDS) to understand and critically reflect on this trend.
Abstract: Contributing to a rising number of Critical Data Studies which seek to understand and critically reflect on the increasing datafication and digitalisation of governance, this paper focuses on the f...
TL;DR: The idea that we leak, drop and leave traces wherever we go has given rise to a culture of traceability, and this culture has become increasingly common to talk about digital traces as discussed by the authors.
Abstract: It has become increasingly common to talk about “digital traces”. The idea that we leak, drop and leave traces wherever we go has given rise to a culture of traceability, and this culture of tracea...
TL;DR: The authors have critiqued the use of algorithms and other automated processes involved in data science on both epistemologic and epistemological levels, and argued that these processes can be used to generate false positives and false negatives.
Abstract: In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemolog...
TL;DR: A qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies, revealing little consensus with regard to how data Archiving policies should be enforced and who should hold authors accountable for dataset submissions.
Abstract: To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developi...
TL;DR: In this paper, the concept of "aesthetic practices" is proposed to capture the work needed for population data to come into relation so that it can be disseminated via government data portals, in our case, the Census Hub of the European Statistical System (ESS) and the Danish Ministry of Education's Data Warehouse.
Abstract: We develop the concept of ‘aesthetic practices’ to capture the work needed for population data to come into relation so that it can be disseminated via government data portals, in our case, the Census Hub of the European Statistical System (ESS) and the Danish Ministry of Education’s Data Warehouse. The portals form part of open government data (OGD) initiatives, which we understand as governing technologies. We argue that to function as such, aesthetic practices are required so that data produced at dispersed sites can be brought into relation and projected as populations at data portals in forms such as bar charts, heat maps and tables. Two examples of aesthetic practices are analysed based on ethnographic studies we have conducted on the production of data for the Hub and Warehouse: metadata and data cleaning. Metadata enables data to come into relation by containing and accounting for (some of) the differences between data. Data cleaning deals with the indeterminacies and absences of data and involves algorithms to determine what values data can obtain so they can be brought into relation. We attend to how both aesthetic practices involve normative decisions that make absent what exceeds them: embodied knowledge that cannot or has not been documented; and data that cannot meet the forms required of data portals. While these aesthetic practices are necessary to sustain data portals as ‘sites of projection,’ we also bring critical attention to their performative effects for knowing, enacting and governing populations.
TL;DR: It is argued that City APIs as elements of infrastructures reveal how urban renewal processes become crucial sites of socio-political contestation between data science, technological development, urban management, and civic participation.
Abstract: This article addresses the role of application programming interfaces (APIs) for integrating data sources in the context of smart cities and communities. On top of the built infrastructures in citi...
TL;DR: This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data.
Abstract: This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of ...
TL;DR: This contribution aims at proposing a framework for articulating different kinds of “normativities” that are and can be attributed to “algorithmic systems” by cutting across history and philosophy of science, legal, and critical theory, as well as “Algorithmics,” and by confronting studies led in engineering laboratories with critical algorithm studies.
Abstract: This contribution aims at proposing a framework for articulating different kinds of “normativities” that are and can be attributed to “algorithmic systems.” The technical normativity manifests itse...
TL;DR: In this article, a special theme examines and ultimately rejects the familiar grand claims of data-fication as a new form of knowledge production and argues that it is not a new technique.
Abstract: Framing datafication as new form of knowledge production has become a trope in both academic and commercial contexts. This special theme examines and ultimately rejects the familiar grand claims of...
TL;DR: It is argued that finer-grained distinctions between types of information systems in the language of law and policy and risk assessment tools integrated into their implementation would strengthen future regulatory efforts by rendering underlying algorithmic components more legible and accountable to political and community stakeholders.
Abstract: A wave of recent scholarship has warned about the potential for discriminatory harms of algorithmic systems, spurring an interest in algorithmic accountability and regulation. Meanwhile, parallel c...
TL;DR: It is concluded that a sustainable implementation of biobanks needs not only to comply with the General Data Protection Regulation, but must proactively re-imagine its relation to citizens and data subjects in order to account for the various ways that science gets entangled with society.
Abstract: Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-re...
TL;DR: The aim of this editorial is to contribute to a more nuanced discussion about algorithms by discussing how the authors, as social scientists, think about algorithms in relation to five theoretical ideal types, and introduces the contributions to this special theme by situating them in connection to these five ideal types.
Abstract: The power of algorithms has become a familiar topic in society, media, and the social sciences. It is increasingly common to argue that, for instance, algorithms automate inequality, that they are ...
TL;DR: In the context of health and medicine, the potential to quantify and 'datafy' areas of life that have not traditionally been considered the remit of biomedicine as discussed by the authors, such as sleep, ageing and emotions.
Abstract: As in other domains, digital data are taking on an ever more central role in health and medicine today. And as it has in other domains, ‘datafication’ is contributing to a re-configuration of health and medicine, prompting its expansion to include new spaces, new practices, new techniques and new actors. Indeed, possibilities to quantify and ‘datafy’ areas of life that have not traditionally been considered the remit of biomedicine – such as sleep, ageing and emotions – and activities that have not traditionally been considered markers of health and disease – such as a person’s consumption patterns, her social media activity or her dietary habits – coupled with the promise of linking these heterogeneous datasets to glean medical insights, have contributed to a redefinition of almost any data as health-related data (Lucivero and Prainsack, 2015; Weber et al., 2014). Increasingly, these new types of data are being generated outside the traditional spaces of medicine, as people go about their daily lives interacting with consumer mobile devices. Similarly, the technological tools needed to capture, store, analyze and manage the flow of these data, from wearables and smart phones to cloud platforms and machine learning, increasingly rely on infrastructure and know-how that lie beyond the scope of traditional medical systems and scientists, amongst data scientists and information and communication technologies specialists. Moreover, new stakeholders are cropping up in these quasi-medical yet still undomesticated territories. On one end of the spectrum, individuals who generate health data as they track and monitor medical conditions, well-being, physical activity, or air quality, are both solicited as research participants and are making demands on researchers to utilize their personal health data (Health Data Exploration Project, 2014). On the other end of the spectrum, consumer technology corporations such as Apple and Google are reinventing themselves as obligatory passage points for dataintensive precision medicine (Sharon, 2016). And somewhere in between, not-for-profit organizations, such as Sage Bionetworks and OpenHumans.org, are positioning themselves as mediators in this ecosystem in formation, between the medical research community, individual and collective generators of data and technology developers. As proponents uphold, this expansion and decentralization of the health data ecosystem is promising, both in terms of the potential to advance data-driven research and healthcare, and in terms of rendering research more inclusive and more meaningful for participants (Shen, 2015; Topol, 2015). But, as critical scholars of science and technology have consistently shown, a fuller grasp of our technological present must always include the far-reaching, unexpected and sometimes deleterious social, political and cultural effects of discourses of scientific progress and technologically enabled democratization and participation. In recent years, such critical scholarship has been particularly wary of the new power asymmetries that datafication contributes to. Rather than levelling power relations, critics observe, these are being redrawn along new digital divides based on data ownership or access, control over digital infrastructures and new types of computational expertise, where those who generate data, especially citizens, patients and consumers, are positioned on the losing side of the on-going extraction and scramble for the world’s data driven by state
TL;DR: In this paper, the authors find little evidence that government use of Big Data will affect public policy outcomes, despite growing scholarly interests, however, little evidence ex ectively ex ect.
Abstract: Scholars are becoming increasingly interested in whether and how government use of Big Data will affect public policy outcomes. Despite such growing scholarly interests, however, little evidence ex...
TL;DR: It is argued that an exclusively commercial approach to data preservation poses important ethical and political risks that demand urgent consideration and is called for a scalable, sustainable, and dignified curation model that incorporates the interests of multiple stakeholders.
Abstract: We project the future accumulation of profiles belonging to deceased Facebook users. Our analysis suggests that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attra...
TL;DR: It is argued that the current reliance on the apparatus of statistical regression limits the scope of possibilities for neural networks in general, and in moving towards artificial creativity in particular.
Abstract: This article discusses three dimensions of creativity: metaphorical thinking; social interaction; and going beyond extrapolation in predictions. An overview of applications of neural networks in these three areas is offered. It is argued that the current reliance on the apparatus of statistical regression limits the scope of possibilities for neural networks in general, and in moving towards artificial creativity in particular. Artificial creativity may require revising some foundational principles on which neural networks are currently built.
TL;DR: Three principles are articulate – trustworthiness, openness and evidence – to address the problem of access to health-related data by private insurers and tame its potentially harmful effects on the development of precision medicine and, more generally, on the advancement of medical science.
Abstract: In this paper, we discuss how access to health-related data by private insurers, other than affecting the interests of prospective policy-holders, can also influence their propensity to make person...
TL;DR: This commentary argues that it cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations.
Abstract: Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop a...
TL;DR: In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central as discussed by the authors, and the idea that these values should be central to the practice of data sci...
Abstract: In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data sci...