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, and asymmetries in terms of data usage and distribution. In this article, we discuss MyData, a data activism initiative originating in Finland, which aims to shape a more sustainable citizen-centric data economy by means of increasing individuals' control of their personal data. Using data gathered during long-term participant-observation in collaborative projects with (...) data activists, we explore the internal tensions of data activism by first outlining two different social imaginaries – technological and socio-critical – within MyData, and then merging them to open practical and analytical space for engaging with the socio-technical futures currently in the making. While the technological imaginary favours data infrastructures as corrective measures, the socio-critical imaginary questions the effectiveness of technological correction. Unpacking them clarifies the kinds of political and social alternatives that different social imaginaries ascribe to the notions underlying data activism, and highlights the need to consider the social structures in play. The more far-reaching goal of our exercise is to provide practical and analytical resources for critical engagement in the context of data activism. By merging technological and socio-critical imaginaries in the work of reimagining governing structures and knowledge practices alongside infrastructural arrangements, scholars can depart from the most obvious forms of critique, influence data activism practice, and formulate data ethics and data futures. (shrink)

Recently, there has been renewed interest in so-called evidence-based policy making. Enticed by the grand promises of Big Data, public officials seem increasingly inclined to experiment with more data-driven forms of governance. But while the rise of Big Data and related consequences has been a major issue of concern across different disciplines, attempts to develop a better understanding of the phenomenon's historical foundations have been rare. This short commentary addresses this gap by situating the current push for numerical evidence within (...) a broader socio-political context, demonstrating how the epistemological claims of Big Data science intersect with specific forms of trust, truth, and objectivity. We conclude by arguing that regulators' faith in numbers can be attributed to a distinct political culture, a representative democracy undermined by pervasive public distrust and uncertainty. (shrink)

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 the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. 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. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics. (shrink)

There is an abundance of enthusiasm and optimism about how governments at all levels can make use of big data, algorithms and artificial intelligence. There is also growing concern about the risks that come with these new systems. This article makes the case for greater government transparency and accountability about uses of big data through a Government of Canada qualitative research case study. Adapting a method from critical cartographers, I employ counter-mapping to map government big data practices and internal discussions (...) of risk and challenge. I do so by drawing on interviews and freedom of information requests. The analysis reveals that there are more concerns and risks than often publicly discussed and that there are significant areas of silence that need greater attention. The article underlines the need for our democratic systems to respond to our new datafied contexts by ensuring that our institutions make changes to better protect citizen rights, uphold democratic principles and ensure means for citizen intervention. (shrink)

The incorporation of neural-based technologies into psychiatry offers novel means to use neural data in patient assessment and clinical diagnosis. However, an over-optimistic technologisation of neuroscientifically-informed psychiatry risks the conflation of technological and psychological norms. Neurotechnologies promise fast, efficient, broad psychiatric insights not readily available through conventional observation of patients. Recording and processing brain signals provides information from ‘beneath the skull’ that can be interpreted as an account of neural processing and that can provide a basis to evaluate general behaviour (...) and functioning. But it ought not to be forgotten that the use of such technologies is part of a human practice of neuroscience informed psychiatry. This paper notes some challenges in the integration of neural technologies into psychiatry and suggests vigilance particularly in respect to normative challenges. In this way, psychiatry can avoid a drift toward reductive technological approaches, while nonetheless benefitting from promising advances in neuroscience and technology. (shrink)

This article asks how the application of digital technologies is changing the organization of the agri-food system in the context of the third food regime. The academic debate on digitalization and food largely focuses on the input and farm level. Yet, based on the analysis of 280 digital services and products, we show that digital technologies are now being used along the entire food commodity chain. We argue that digital technologies in the third food regime serve on the one hand (...) as a continuation of established information and communication technologies, thus deepening certain features of the existing food regime such as the retail sector’s control over global commodity chains. On the other hand, digital technologies also introduce new forms of control and value extraction based on the use of data and pave the way for large tech companies to take over market shares in the agri-food sector. Finally, we find that multinational agri-food companies are starting to take on the business models of leading digital tech companies, for instance by developing digital platforms throughout the agri-food system. We argue that this shows that the broader economic restructuring of neoliberal capitalism towards digital capitalism is also making its way into the agri-food system. (shrink)

I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific (...) knowledge. In particular, it is shown to lack a pronounced hierarchical, nested structure. The significance of the transition to such “horizontal” modeling is underlined by the concurrent emergence of novel inductive methodology in statistics such as non-parametric statistics. Data-intensive modeling is well equipped to deal with various aspects of causal complexity arising especially in the higher level and applied sciences. (shrink)

This article examines regulation’s understanding of technology in American financial markets as means for rethinking the contours and institutional limits of governance in the age of financialization. The article identifies how the Securities and Exchange Commission perceived markets and their conceptual relation to technology throughout much of the long twentieth century by distilling the “ontologies” expressed by the agency’s leadership. Despite the fact that SEC’s commissioners recognized technologies as playing a central role in the market’s current and future operations, these (...) were never effectively brought under regulatory scrutiny even when such action fell under the commission's jurisdictional remit. Rather than regulating technologies as constitutive of markets, the governance of the material devices of finance was discursively kept at arm's-length by presenting them as intractable objects that were external to financial innovation. Triangulating across several techniques of computational text analysis, this article shows how this interpretation of technology mirrored distinct shifts in how regulators understood markets, from being physical trading sites populated by agents that required vetting and certification to a distributed, multi-sited system surveilled through transparency and disclosure. Throughout these ontological transitions, technology’s inscrutability remained, limiting the capacity of the state and its regulatory agencies to shape the evolution of finance and its underlying infrastructures. (shrink)

In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, (...) and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research. (shrink)

Automated technologies populating today’s online world rely on social expectations about how “smart” they appear to be. Algorithmic processing, as well as bias and missteps in the course of their development, all come to shape a cultural realm that in turn determines what they come to be about. It is our contention that a robust analytical frame could be derived from culturally driven Science and Technology Studies while focusing on Callon’s concept of translation. Excitement and apprehensions must find a specific (...) language to move past a state of latency. Translations are thus contextual and highly performative, transforming justifications into legitimate claims, translators into discursive entrepreneurs, and power relations into new forms of governance and governmentality. In this piece, we discuss three cases in which artificial intelligence was deciphered to the public: the Montreal Declaration for a Responsible Development of Artificial Intelligence, held as a prime example of how stakeholders manage to establish the terms of the debate on ethical artificial intelligence while avoiding substantive commitment; Mark Zuckerberg’s 2018 congressional hearing, where he construed machine learning as the solution to the many problems the platform might encounter; and the normative renegotiations surrounding the gradual introduction of “killer robots” in military engagements. Of interest are not only the rational arguments put forward, but also the rhetorical maneuvers deployed. Through the examination of the ramifications of these translations, we intend to show how they are constructed in face of and in relation to forms of criticisms, thus revealing the highly cybernetic deployment of artificial intelligence technologies. (shrink)

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 politics” by exploring what we call the “contentious politics of data”. By contentious politics of data we indicate the bottom-up, transformative initiatives interfering with and/or hijacking dominant processes of datafication, contesting existing power relations or re-appropriating data practices and infrastructure for purposes distinct from the intended. Said contentious politics of data is (...) articulated in an array of practices of data activism taking a critical stance towards datafication. In data activism, data as mediators take a central role, both as part of an action repertoire or as objects of struggle in their own right. Leveraging social movement studies and science and technology studies, this theoretical essay argues that data activism can be mapped along two analytical dimensions: “data as stakes” vs. “data as repertoires”, and “individual practice vs. collective action”. Mapping action repertoires and tactics along these axes allows us to chart the potential emergence of a political data subject at the intersection of these two dimensions. This furthers our understanding of people’s engagement with data in relation to other forms of activism and existing work in social movement studies. It also helps us interpreting potential trajectories of contemporary social movements, as they increasingly interface with data, devices and platforms. (shrink)

Drawing on interviews with people from the advertising and technology industry, legal experts and policy makers, this paper assesses the rise of emotion detection in digital out-of-home advertising, a practice that often involves facial coding of emotional expressions in public spaces. Having briefly outlined how bodies contribute to targeting processes and the optimisation of the ads themselves, it progresses to detail industrial perspectives, intentions and attitudes to data ethics. Although the paper explores possibilities of this sector, it pays careful attention (...) to existing practices that claim not to use personal data. Centrally, it argues that scholars and regulators need to pay attention to the principle of intimacy. This is developed to counter weaknesses in privacy that is typically based on identification. Having defined technologies, use cases, industrial perspectives, legal views and arguments about jurisprudence, the paper discusses this ensemble of perspectives in light of a nationwide survey about how UK citizens feel about the potential for emotion detection in out-of-home advertising. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model for facilitating (...) this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (shrink)

The urban environment is increasingly engaging with artificial intelligence, a focus on the automation of urban processes, whether it be singular artefacts or city-wide systems. The impact of such technological innovation on the social dynamics of the urban environment is an ever changing and multi-faceted field of research. In this paper, the space and time defined by the autonomous vehicle is used as a window to view the way in which a shift in urban transport dynamics can impact the temporal (...) experience of an individual. Using the finite window of time created by an autonomous vehicle, a theoretical framework is put forward that seeks to show how contrasting narratives exist regarding the experience of the window of time within the autonomous vehicle. By taking a theoretical approach informed by social theory, the dissolution of barriers between separate spheres of life is explored to highlight the increased commodification of time. In focusing on both the space and time created by the autonomous vehicle this approach seeks to highlight how artificial intelligence can provide a contemporary space in the urban environment while also opening a new window of time. The cognitive dissonance observed when comparing the narratives of autonomous vehicle stakeholders and the historical shift in time use leads to a belief that technology makes the user more free in terms of time. With this paper the autonomous vehicle is shown to be an ideal space and time to view the way in which the use of such technology does not increase free-time, but further dissolves the boundaries between what is and what is not work-time. (shrink)

The article analyzes the relation of humans and technology concerning so called ‘intelligent’ or ‘autonomous’ algorithms that are applied in everyday contexts but are far removed from any form of substantial artificial intelligence. In particular, the use of algorithms in surveillance and in architecture is discussed. These examples are structured by a particular combination of continuity and difference between humans and technology. The article provides a detailed analysis of boundary practices that establish continuity and oppositions between humans and information technology, (...) referring to their exemplary depiction in movies. Both strands of boundary practices have the potential to challenge as well as sustain the position of the human as liberal, autonomous subject. Finally, it is shown how the particular combination of continuity and difference that structures the use of algorithms maintains the power of liberal, autonomous subject positions, while the shift of decisions to the algorithms seems to decenter the human. (shrink)

Big data techniques, data-driven science and their technological applications raise many serious ethical questions, notably about privacy protection. In this paper, we highlight an entanglement between epistemology and ethics of big data. Discussing the mobilisation of big data in the fields of biomedical research and health care, we show how an overestimation of big data epistemic power – of their objectivity or rationality understood through the lens of neutrality – can become ethically threatening. Highlighting the irreducible non-neutrality at play in (...) big data tools, we insist upon the ethical importance of a critical epistemological approach in which big data are understood as possibly valuable only when coupled with human intelligence and evaluative rationality. (shrink)

This paper discusses the theory of knowledge based on the idea of dynamical space. The goal of this effort is to comprehend the knowledge that remains beyond the human domain, e.g., of the artificial cognitive systems. This theory occurs in two versions, weak and strong. The weak version is limited to knowledge in which retention and articulation are performed through the discourse. The strong version is general and is not limited in any way. In the weak version, knowledge is represented (...) by the trajectories of discourses in time, in a dynamical space called the discursive space, which has an arbitrary number of dimensions. Given space is used to represent a given part of knowledge. A manifold is introduced to represent knowledge with a wider scope. The strong version is an extrapolation of the weak version to cover all forms of knowledge, not necessarily human or manifesting in language. The use of dynamical space construction allows one to formalize knowledge as such. Such an effort requires us to initially consider knowledge as mainly a social and linguistic phenomenon, which also could be presented as a result of the evolution of the understanding of knowledge that took place in the 20th century. (shrink)

Humans have become increasingly datafied with the use of digital technologies that generate information with and about their bodies and everyday lives. The onto-epistemological dimensions of human–data assemblages and their relationship to bodies and selves have yet to be thoroughly theorised. In this essay, I draw on key perspectives espoused in feminist materialism, vital materialism and the anthropology of material culture to examine the ways in which these assemblages operate as part of knowing, perceiving and sensing human bodies. I draw (...) particularly on scholarship that employs organic metaphors and concepts of vitality, growth, making, articulation, composition and decomposition. I show how these metaphors and concepts relate to and build on each other, and how they can be applied to think through humans’ encounters with their digital data. I argue that these theoretical perspectives work to highlight the material and embodied dimensions of human–data assemblages as they grow and are enacted, articulated and incorporated into everyday lives. (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)

Recently, philosophers and social scientists have turned their attention to the epistemological shifts provoked in established sciences by their incorporation of big data techniques. There has been less focus on the forms of epistemology proper to the investigation of algorithms themselves, understood as scientific objects in their own right. This article, based upon 12 months of ethnographic fieldwork with Russian data scientists, addresses this lack through an investigation of the specific forms of epistemic attention paid to algorithms by data scientists. (...) On the one hand, algorithms are unlike other mathematical objects in that they are not subject to disputation through deductive proof. On the other hand, unlike concrete things in the world such as particles or organisms, algorithms cannot be installed as the objects of experimental systems directly. They can only be evaluated in their functioning as components of extended computational assemblages; on their own, they are inert. As a consequence, the epistemological coding proper to this evaluation does not turn on truth and falsehood but rather on the efficiency of a given algorithmic assemblage. This article suggests that understanding the forms of algorithmic rationality employed in such inquiry is crucial for charting the place of data science within the contemporary academy and knowledge economy more generally. (shrink)

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for ethical, anti-oppressive digital data-driven research in (...) the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data. (shrink)

Against the background of the increasing importance of digitization in health care, the paper examines how medical practitioners who are involved in the development of digital health technologies legitimate and criticize the implementation and use of digital health technologies. Adopting an institutional logics perspective, the study is based on qualitative interviews with persons working at the interface of medicine and digital technologies development in Switzerland. The findings indicate that the developers believe that digital health technologies could harmonize current conflicts between (...) an increasing economization of the health care system and professional–ethical demands. At the same time, however, they show that digital technologies can undermine the demand for medical autonomy, a central element of the medical ethos. (shrink)

Big Data’s calculative ontology relies on and reproduces a form of hyperindividualism in which the ontological unit of analysis is the discrete data point, the meaning and identity of which inheres in itself, preceding, separate, and independent from its context or relation to any other data point. The practice of Big Data governed by an ontology of hyperindividualism is also constitutive of that ontology, naturalizing and diffusing it through practices of governance and, from there, throughout myriad dimensions of everyday life. (...) In this paper, I explicate Big Data’s ontology of hyperindividualism by contrasting it to a coconstitutive ontology that prioritizes relationality, context, and interdependence. I then situate the ontology of hyperindividualism in its genealogical context, drawing from Patrick Joyce’s history of liberalism and John Dewey’s pragmatist account of individualism, liberalism, and social action. True to its genealogical provenance, Big Data’s ontological politics of hyperindividualism reduces governance to the management of atomistic behavior, undermines the contribution of urban complexity as a resource for governance, erodes the potential for urban democracy, and eviscerates the possibility of collective resistance. (shrink)

This article compares political clientelism and datafied campaigning as two modes of relating politicians/parties and voters that are centred around voter surveillance. It contributes to the discussion on consequences of Big Data by showing similarities of datafied campaigns with a type of electoral politics that pre-dates the advent of mass media and is usually regarded as deficient. It thus departs from the predominant perspective on datafication and surveillance, which draws on Foucault, in order to identify the particular challenges that datafication (...) poses in the realm of democratic electoral politics. They are related to four major aspects in which datafied campaigning resembles political clientelism, as opposed to the combination of ideology, issue-based campaigning and media appeal that characterized Western European party politics in the second half of the 20th century. It personalizes the relationship between politicians and voters with the help of intermediaries; it is based on an asymmetric and iterative monitoring of voters; it implies a strong particularism and an affinity with populist appeals; and it is ambivalent with regard to the volition of voters. The identification of these similarities renders general concerns about the consequences of datafied campaigning for democracy more concrete. It offers a mirror in which seemingly novel practices are revealed to have implications that are well known to be problematic for the quality of democracy. (shrink)

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose (...) to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity. (shrink)

Advances in information and communication technologies enable more decentralized and individualized mechanisms for coordination and for managing societal complexity. This has important consequences for the role of conditionality and the idea of individual responsibility in two seemingly unrelated policy areas. First, the changing information infrastructure enables an extension of conditionality in the area of welfare through greater activation, enhanced self-management, and a personalization of risks. Second, conditionality and personal responsibility also form an important ideational template and a legitimatory basis for (...) facilitating value creation that is based on data as a raw material. This argument is illustrated looking at the trajectories of the digital strategies in the United Kingdom and Germany. In both cases, data protection is depicted as a question of individual responsibility and tied to certain forms of individual conduct. (shrink)

A growing literature is taking an institutionalist and governance perspective on how algorithms shape society based on unprecedented capacities for managing social complexity. Algorithmic governance altogether emerges as a novel and distinctive kind of societal steering. It appears to transcend established categories and modes of governance—and thus seems to call for new ways of thinking about how social relations can be regulated and ordered. However, as this paper argues, despite its novel way of realizing outcomes of collective steering and coordination, (...) it can nevertheless be grasped with an old and fundamental figure in political philosophy: that of Thomas Hobbes’ Leviathan. Comparing algorithmic governance with this figure serves to highlight their similarities as socio-political arrangements, and specifically to clarify how algorithmic governance parallels the apolitical traits of the Leviathan—it eliminates the political as it requires compliance and forgoing contestation to best fulfill its role and to produce satisfying outcomes. (shrink)

ABSTRACTCompared to other social sciences, it appears economists enjoy overwhelming influence in policy debates. What are the ethical concerns that should bear upon the exercise of such voice? Two forms of conflict of interest are explored here: funding sources may undermine objectivity in framing the research agenda as well as through the better-known route of influencing research outcomes. What questions get asked or ignored, and the methodological approach that economists choose, may be influenced by funders. Considerations of personal advancement may (...) influence how, when and what research is disseminated. The absence of debate on accountability of economists-as-advisors is worrying. Self-regulatory processes are needed so that power is exercised with greater responsibility. (shrink)

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 developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. The effort to increase data sharing in the sciences is (...) one part of a broader “data revolution” that has prompted discussion about a paradigm shift in scientific research. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted 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. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process. (shrink)

Insurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients’ behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and Science and Technology Studies, this article investigates the role of economic (...) experimentation in the making of data-driven personalisation markets in insurance. We document a case study of a car insurance experiment, launched by a Belgian direct insurance company in 2016 to set up an experiment of tracking driving style behavioural data of over 5000 participants over a one-year period. Based on interviews and document analysis, we outline how this in vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Using JL Austin’s distinction between happy and unhappy statements, we argue how the experiment, despite its failure not to provide the desired evidence, could be considered a ‘happy’ event. We conclude by highlighting the role of economic experiments ‘in the wild’ for the making of future markets of data-driven personalisation. (shrink)

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on first principles, and the naïve-inductivist one, based only on data. This latter view has recently gained some attention in response to the availability (...) of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann. (shrink)

How do the organization and presentation of large-scale social media images recondition the process by which visual knowledge, value, and meaning are made in contemporary conditions? Analyzing fundamental elements in the changing syntax of existing visual software ontology—the ways current social media platforms and aggregators organize and categorize social media images—this article relates how visual materials created within social media platforms manifest distinct modes of knowledge production and acquisition. First, I analyze the structure of social media images within data streams (...) as opposed to previous information organization in a structured database. While the database has no pre-defined notions of time and thus challenges traditional linear forms, the data stream re-emphasizes the linearity of a particular data sequence and activates a set of new relations to contemporary temporalities. Next, I show how these visual arrangements and temporal principles are manifested and discussed in three artworks: “Untitled” by Felix Gonzalez-Torres, The Clock by Christian Marclay, and Last Clock by Jussi Ängeslevä and Ross Cooper. By emphasizing the technical and poetic ways in which social media situate the present as a “thick” historical unit that embodies multiple and synchronous temporalities, this article illuminates some of the conditions, challenges, and tensions between former visual structures and current ones, and unfolds the cultural significations of contemporary big visual data. (shrink)

Predictive policing has become a new panacea for crime prevention. However, we still know too little about the performance of computational methods in the context of predictive policing. The paper provides a detailed analysis of existing approaches to algorithmic crime forecasting. First, it is explained how predictive policing makes use of predictive models to generate crime forecasts. Afterwards, three epistemologies of predictive policing are distinguished: mathematical social science, social physics and machine learning. Finally, it is shown that these epistemologies have (...) significant implications for the constitution of predictive knowledge in terms of its genesis, scope, intelligibility and accessibility. It is the different ways future crimes are rendered knowledgeable in order to act upon them that reaffirm or reconfigure the status of criminological knowledge within the criminal justice system, direct the attention of law enforcement agencies to particular types of crimes and criminals and blank out others, satisfy the claim for the meaningfulness of predictions or break with it and allow professionals to understand the algorithmic systems they shall rely on or turn them into a black box. By distinguishing epistemologies and analysing their implications, this analysis provides insight into the techno-scientific foundations of predictive policing and enables us to critically engage with the socio-technical practices of algorithmic crime forecasting. (shrink)

This article develops and mobilises the concept of ‘mundane data’ as an analytical entry point for understanding Big Data. We call for in-depth investigation of the human experiences, routines, improvisations and accomplishments which implicate digital data in the flow of the everyday. We demonstrate the value of this approach through a discussion of our ethnographic research with self-tracking cycling commuters. We argue that such investigations are crucial in informing our understandings of how digital data become meaningful in mundane contexts of (...) everyday life for two reasons: first because there is a gap in our understanding of the contingencies and specificities through which big digital data sets are produced, and second because designers and policy makers often seek to make interventions for change in everyday contexts through the presentation of mundane data to consumers but with little understanding of how people produce, experience and engage with these data. (shrink)

Machine learning systems have shown great potential for performing or supporting inferential reasoning through analyzing large data sets, thereby potentially facilitating more informed decision-making. However, a hindrance to such use of ML systems is that the predictive models created through ML are often complex, opaque, and poorly understood, even if the programs “learning” the models are simple, transparent, and well understood. ML models become difficult to trust, since lay-people, specialists, and even researchers have difficulties gauging the reasonableness, correctness, and reliability (...) of the inferences performed. In this article, we argue that bridging this gap in the understanding of ML models and their reasonableness requires a focus on developing an improved methodology for their creation. This process has been likened to “alchemy” and criticized for involving a large degree of “black art,” owing to its reliance on poorly understood “best practices”. We soften this critique and argue that the seeming arbitrariness often is the result of a lack of explicit hypothesizing stemming from an empiricist and myopic focus on optimizing for predictive performance rather than from an occult or mystical process. We present some of the problems resulting from the excessive focus on optimizing generalization performance at the cost of hypothesizing about the selection of data and biases. We suggest embedding ML in a general logic of scientific discovery similar to the one presented by Charles Sanders Peirce, and present a recontextualized version of Peirce’s scientific hypothesis adjusted to ML. (shrink)

Data is essential to governing those emerging matters of concern that confront the agrifood every day. But data is no neutral intermediary. It disrupts, exposes, and creates new social, economic, political, and environmental possibilities, whilst simultaneously hiding, excluding, and foreclosing others. Scholars have become attuned to both the constitutive role of data in creating everyday worlds, and the need to develop critical accounts of the materialities, spatialities and multiplicities of data relationships. Whereas this emerging work develops insight to the capacity (...) for data topologies to reterritorialise the spatial performances of everyday life, it has largely reduced the associated temporal dimensions to matters of fact. The effect of these performances has been to naturalize the temporal quality of speed and elide the multiple temporalities required to enact contemporary data worlds. Applying the lenses of infrastructuring, performativity and ferality, this paper explores temporality and data in the everyday worlds produced through the New Zealand kiwifruit industry’s focus on dry matter. The paper argues that temporalities are deeply embedded in the kiwifruit industry’s data relations. We show that while temporal data relations are critical to the industry, we also highlight ways in which those relations introduce new, potentially destabilizing performances into kiwifruit relations. (shrink)

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating technological dependencies that pressure (...) organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration. (shrink)

Despite its empirical prominence, there is very little extant organizational research on Big Data. However, there is reason to believe this is changing as organizational theory scholars are beginning to embrace new methods and data sources. In this essay, I present a view that suggests there are several latent opportunities, many of which have been simmering unattended for some time. This research approach is not without its challenges, as the ontological terrain of Big Data is untested and potentially disruptive. However, (...) we are observing a renewal of approaches to text and content analysis. By opening up the toolkit of computational linguistics methods for text analysis, Big Data may bring about fresh synthesis and reshape classic debates around social structure. (shrink)

Public discourse about the COVID-19 that appears on Twitter and other social media platforms provides useful insights into public concerns and responses to the pandemic. However, acknowledging that public discourse around COVID-19 is multi-faceted and evolves over time poses both analytical and ontological challenges. Studies that use text-mining approaches to analyse responses to major events commonly treat public discourse on social media as an undifferentiated whole, without systematically examining the extent to which that discourse consists of distinct sub-discourses or which (...) phases characterize its development. They also confound structured behavioural data with unstructured user-generated data in their sampling methods. The present study aims to demonstrate how one might go about addressing both of these sets of challenges by combining corpus linguistic methods with a data-driven text-mining approach to gain a better understanding of how the public discourse around COVID-19 developed over time and what topics combine to form this discourse in the Australian Twittersphere over a period of nearly four months. By combining text mining and corpus linguistics, this study exemplifies how both approaches can complement each other productively. (shrink)

In this paper, we discuss the development and piloting of a new methodology for illuminating the socio-material constitution of data objects and flows as data move between different sites of practice. The data journeys approach contributes to the development of critical, qualitative methodologies that can address the geographic and temporal scale of emerging knowledge infrastructures, and capture the ‘life of data’ from their initial generation through to re-use in different contexts. We discuss the theoretical development of the data journeys methodology (...) and the application of the approach on a project examining meteorological data on their journey from initial production through to being re-used in climate science and financial markets. We then discuss three key conceptual findings from this project about: the socio-material constitution of digital data objects, ‘friction’ in the movement of data through space and time and the mutability of digital data as a material property that contributes to driving the movement of data between different sites of practice. (shrink)

This paper draws together empirical findings from our study of hackathons in the UK with literature on big data through three interconnected frameworks: data as discourse, data as datalogical and data as materiality. We suggest not only that hackathons resonate the wider socio-technical and political constructions of data that are currently enacted in policy, education and the corporate sector, but also that an investigation of hackathons reveals the extent to which ‘data’ operates as a powerful discursive tool; how the discourses (...) of data mask and reveal a series of tropes pertaining to data; that the politics of data are routinely and simultaneously obscured and claimed with serious implications for expertise and knowledge; and that ultimately, and for the vast majority of hackathons we have attended, the discursive and material constructions of data serve to underpin rather than challenge existing power relations and politics. (shrink)

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: natural big data, artificial (...) big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere. (shrink)

Data-intensive science comes with increased risks concerning quality and reliability of data, and while trust in science has traditionally been framed as a matter of scientists being expected to adhere to certain technical and moral norms for behaviour, emerging discourses of open science present openness and transparency as substitutes for established trust mechanisms. By ensuring access to all available information, quality becomes a matter of informed judgement by the users, and trust no longer seems necessary. This strategy does not, however, (...) take into consideration the networks of professionals already enabling data-intensive science by providing high-quality data. In the life sciences, biological data- and knowledge bases managed by expert biocurators have become crucial for data-intensive research. In this paper, I will use the case of biocurators to argue that openness and transparency will not diminish the need for trust in data-intensive science. On the contrary, data-intensive science requires a reconfiguration of existing trust mechanisms in order to include those who take care of and manage scientific data after its production. (shrink)

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

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present the DEDA framework as (...) an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices. (shrink)