Similar Journals
|
Big Data & Society
Number of Followers: 36  Open Access journalISSN (Online) 2053-9517 Published by Sage Publications  [1174 journals]
|
- Smart campus communication, Internet of Things, and data governance:
Understanding student tensions and imaginaries
Authors: Pauline Hope Cheong, Pratik Nyaupane
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
In recent years, universities have been urged to restructure and re-evaluate their ability to trace and monitor their students as the “smart campus” is being built upon datafication, while networked apps and sensors serve as the means through which its constituents are connected and governed. This paper advances a dialectical and communication-centered approach to the Internet of Things campus ecosystem and provides an empirical investigation into (a) the tensions experienced by students and (b) the ways that these students envision alternative practices that support their digital engagement. Drawing upon student focus group interviews in a large American research and innovation intensive university, dialectical tensions identified include convenience–annoyance, integration–independence, and safety–insecurity, brought upon by students’ ongoing and prospective negotiations with Internet of Things. Furthermore, in a bid to understand students’ alternative data imaginaries, this project examined students’ preferred Internet of Things-related communication practices with campus digital application platforms, analog and older forms of digital media, as well as in-person interactions with traditional authorities within classroom and group settings. Finally, this contribution presents a discussion of the findings for theory and praxis, particularly for smart campus innovation and social data governance, in terms of potential growing challenges involving complexifying student privacy concerns, data normalization and coercion, and tertiary digital divides and inequalities.
Citation: Big Data & Society
PubDate: 2022-06-24T05:18:59Z
DOI: 10.1177/20539517221092656
Issue No: Vol. 9, No. 1 (2022)
- ‘Real-time’ air quality channels: A technology review of emerging
environmental alert systems
Authors: Kayla Schulte
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Poor air quality is a pressing global challenge contributing to adverse health impacts around the world. In the past decade, there has been a rapid proliferation of air quality information delivered via sensors, apps, websites or other media channels in near real-time and at increasingly localized geographic scales. This paper explores the growing emphasis on self-monitoring and digital platforms to supply informational interventions for reducing pollution exposures and improving health outcomes at the individual level. It presents a technological case study that characterizes emerging air quality information communication mechanisms, or ‘AQ channels’, while drawing upon examples throughout the literature. The questions are posed: which air quality channels are ‘freely’ available to individuals in London, UK, and when and where are they accessed' Digital trace data and metadata associated with 54 air quality channels are synthesized narratively and graphically. Results reveal air quality channels derive air pollution estimates using common data sources, display disparate messaging, adopt variable geographic scales for reporting ‘readings’ and maintain psychosocial barriers to access and adoption of exposure-reducing behaviours. The results also point to a clear association between the publication of a high-profile news article about air pollution and increased air quality channel access. These findings illuminate a need for greater transparency around how air quality channels generate personalized air pollution exposure estimates and tailor messaging. The paper concludes by calling for air quality channel developers to exercise co-creative methods that can support sustainable, democratic data and knowledge production around air quality, while critically approaching disproportionate patterns of both pollution and information exposure.
Citation: Big Data & Society
PubDate: 2022-06-15T10:23:04Z
DOI: 10.1177/20539517221101346
Issue No: Vol. 9, No. 1 (2022)
- The ontology explorer: A method to make visible data infrastructures for
population management
Authors: Wouter Van Rossem, Annalisa Pelizza
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
This article introduces the methodology of the ‘Ontology Explorer’, a semantic method and JavaScript-based open-source tool to analyse data models underpinning information systems. The Ontology Explorer has been devised and developed by the authors, who recognized a need to compare data models collected in different formats and used by diverse systems. The Ontology Explorer is distinctive firstly because it supports analyses of information systems that are not immediately comparable and, secondly, because it systematically and quantitatively supports discursive analysis of ‘thin’ data models – also by detecting differences and absences through comparison. When applied to data models underpinning systems for population management, the Ontology Explorer enables the apprehension of how people are ‘inscribed’ in information systems: which assumptions are made about them, and which possibilities are excluded by design. The Ontology Explorer thus constitutes a methodology to capture authorities’ own imaginaries of populations and the ‘scripts’ through which they enact actual people. Furthermore, the method allows the comparison of scripts from diverse authorities. This is exemplified by illustrating its functioning with information systems for population management deployed at the European border. Our approach integrates a number of insights from early infrastructure studies and extends their methods and analytical depth to account for contemporary data infrastructures. By doing so, we hope to trigger a systematic discussion on how to extend those early methodical innovations at the semantic level to contemporary developments in digital methods.
Citation: Big Data & Society
PubDate: 2022-06-09T03:18:03Z
DOI: 10.1177/20539517221104087
Issue No: Vol. 9, No. 1 (2022)
- “Make our communities better through data”: The moral economy
of smart city labor
Authors: Ryan Burns, Preston Welker
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Smart cities are now an established context in which data and digital technologies shape urban politics. Despite increased scholarly focus on algorithmic governance, smart cities and their data production still heavily rely on human labor, raising questions about how that labor is recruited and the implications of different recruitment strategies. In this paper, we illuminate the relations and practices mobilized to recruit the labor required to produce, analyze, and enact data that (re)produce smart cities. We argue that smart cities recruit such digital labor by producing and circulating moral values and sentiments to claim that such participation is a social good. In this article we draw on a 6-year ongoing project in Calgary, Canada to explore how these “moral economies” underwrite smart city ecosystems. We explore three projects related to data and digital labor in the Calgary smart city: a wearable technology collaborative project, a civic hacking group, and the community social media platform Nextdoor. We suggest that moral economies of smart cities signal a new juncture between urban planning and profiting from data, with the potential for creating new socio-political risks. These moral economies signal a shift toward a “new spirit of capitalism” in which labor is managed through indirect persuasion rather than direct compulsion and mandate.
Citation: Big Data & Society
PubDate: 2022-06-09T01:11:47Z
DOI: 10.1177/20539517221106381
Issue No: Vol. 9, No. 1 (2022)
- Social impacts of algorithmic decision-making: A research agenda for the
social sciences
Authors: Frederic Gerdon, Ruben L Bach, Christoph Kern, Frauke Kreuter
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM.
Citation: Big Data & Society
PubDate: 2022-05-31T03:57:36Z
DOI: 10.1177/20539517221089305
Issue No: Vol. 9, No. 1 (2022)
- Beyond manifestos: Exploring how political campaigns use online
advertisements to communicate policy information and pledges
Authors: Tom Dobber, Claes de Vreese
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Social media platforms take on increasingly big roles in political advertising. Microtargeting techniques facilitate the display of tailored advertisements to specific subsegments of society. Scholars worry that such techniques might cause political information to be displayed to only very small subgroups of citizens. Or that targeted communication about policy could make the mandate of elected representatives more challenging to interpret. Policy information in general and pledges, in particular, have received much scientific scrutiny. Scholars have focused largely on party manifestos, but policy information and pledges communicated via online advertisements offer a new arena with new dynamics. This study uses Facebook’s ad library to describe how Dutch political campaigns advertise policy information and pledges in the run-up to the 2019 European Elections. The results show that much policy information is displayed to small subsegments of society. These findings provide evidence for concerns about pledge obfuscation, voter manipulation and mandate interpretation.
Citation: Big Data & Society
PubDate: 2022-05-30T06:55:10Z
DOI: 10.1177/20539517221095433
Issue No: Vol. 9, No. 1 (2022)
- Tradeoffs all the way down: Ethical abduction as a decision-making process
for data-intensive technology development
Authors: Anissa Tanweer
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Ample scholarship demonstrates that data-intensive technologies have the capacity to cause serious harm and that their developers are obliged to address ethics in their work. This ethnographic paper tells the story of data scientists attempting to instantiate a carefully considered ethical vision into a data infrastructure while balancing competing priorities, negotiating divergent interests, and wrestling with contrasting values. I use their story to develop the concept of “ethical abduction,” which I characterize as an exemplary process by which actors can intentionally and systematically address ethical issues that arise during their day-to-day actions by making decisions with consideration for a foundational ethical worldview. It entails tacking back and forth between divergent but complementary ways of thinking: between establishing ideals and making decisions given practical constraints; between understanding historical context and anticipating future consequences; between acknowledging structural dependencies and accepting responsibility for moral agency.
Citation: Big Data & Society
PubDate: 2022-05-30T06:09:57Z
DOI: 10.1177/20539517221101351
Issue No: Vol. 9, No. 1 (2022)
- Materialities of digital disease control in Taiwan during COVID-19
Authors: Sung-Yueh Perng
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
During the course of the COVID-19 pandemic, a wide range of digital technologies and data analytics have been incorporated into pandemic response models globally, in the hope of better detecting, tracking, monitoring and containing outbreaks. This increased digital involvement in disease control has offered the prospect of heightened effectiveness in all of the above, but not without raising other concerns. This paper contributes to ongoing discussions of the digital transformation in disease control by proposing a materialist analysis of how such control has become operative and what its effects may be, both now and in the future. Using Taiwan's digital pandemic response as a case study, the paper explores specific ways in which material processes and arrangements have shaped digital measures, as well as the actions that rendered such measures operable, with their ensuing consequences. This analysis illustrates the importance of historical, material and technological specificities and contingencies to our understanding of how digital disease control takes a particular shape. It also demonstrates how shifting regimes of practice continually reconfigure the ways in which digital disease control functions. The paper argues that paying greater attention to the materialities of digital disease control can provide a more nuanced understanding of the complex ways in which society may be protected or harmed by its use, possibly simultaneously. It is hoped that such increased attentiveness may inform more considered and careful preparation for subsequent pandemics.
Citation: Big Data & Society
PubDate: 2022-05-25T06:21:41Z
DOI: 10.1177/20539517221097315
Issue No: Vol. 9, No. 1 (2022)
- Developing data capability with non-profit organisations using
participatory methods
Authors: Anthony McCosker, Xiaofang Yao, Kath Albury, Alexia Maddox, Jane Farmer, Julia Stoyanovich
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
In this paper, we explore the methodologies underpinning two participatory research collaborations with Australian non-profit organisations that aimed to build data capability and social benefit in data use. We suggest that studying and intervening in data practices in situ, that is, in organisational data settings expands opportunities for improving the social value of data. These situated and collaborative approaches not only address the ‘expertise lag’ for non-profits but also help to realign the potential social value of organisational data use. We explore the relationship between data literacy, data expertise and data capability to test the idea that collaborative work with non-profit organisations can be a practical step towards addressing data equity and generating data-driven social outcomes. Rather than adopting approaches to data literacy that focus on individuals – or ideal ‘data citizens’ – we target the organisation-wide data settings, goals and practices of the non-profit sector. We conclude that participatory methods can embed social value-generating data capability where it can be sustained at an organisational level, aligning with community needs to promote collaborative data action.
Citation: Big Data & Society
PubDate: 2022-05-11T07:15:19Z
DOI: 10.1177/20539517221099882
Issue No: Vol. 9, No. 1 (2022)
- Anthropographics in COVID-19 simulations
Authors: Madeleine Sorapure
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Data visualization researchers and designers have explored a range of approaches to ensure that non-expert audiences understand and derive value from their work. Using anthropomorphized data graphics—or anthropographics—is one strategy that can help create a connection between data and audiences. Anthropographics have been defined as “visualizations that represent data about people in a way that is intended to promote prosocial feelings (e.g. compassion or empathy) or prosocial behavior (e.g. donating or helping).” However, during the SARS-CoV-2 pandemic, anthropographics were used in data visualizations that had an expanded range of rhetorical goals beyond promoting prosocial feelings and behavior—for instance, informing people about the pandemic, persuading them to adopt certain behaviors, or memorializing those killed by the virus. In particular, anthropographics were used in visualized simulations to model possible futures for audiences, showing the spread and impact of the virus in various scenarios. These simulations used anthropomorphizing strategies in text as well as in graphics, along with interactive options that enabled audiences to explore personal connections with the data. As demonstrated through a close reading of several of these COVID-19 simulations, anthropographics can be viewed holistically as a design strategy that incorporates text and interactivity as well as graphical marks in representing data. Findings from this analysis suggest several additions to the design space for anthropographics.
Citation: Big Data & Society
PubDate: 2022-05-11T07:14:59Z
DOI: 10.1177/20539517221098414
Issue No: Vol. 9, No. 1 (2022)
- Emotional labour in the collaborative data practices of repurposing
healthcare data and building data technologies
Authors: Marta Choroszewicz
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
This article focuses on emotions, conceptualised as emotional labour, evoked during data practices used to repurpose and enable healthcare data journeys for Finnish public healthcare. Combined approaches from critical data studies and the sociology of emotions were used to contribute to a better understanding of the mundane but often invisible work of the emotions of experts involved in data practices, such as facilitating data journeys and building data technologies. The article is based on a two-and-a-half-year ethnographic study conducted in a Finnish regional public healthcare and social service organisation. The study results were derived from the analysis of 39 interviews and fieldnotes produced by observing 170 h of various meetings, events and work activities performed by experts. The results were organised into three forms of observed experts’ emotional labour related to three phases of healthcare data journeys: (a) caring for data production and preparing data for travel, (b) managing excitement and frustration in data processing for continually building the data management system, and (c) reassuring users in making sense of obtained data analytics. The results contribute to a greater understanding of the emotions and emotional labour generated by healthcare data journeys and in relation to the volatile nature of healthcare data and the collaborative character of data practices. This work advocates for a better recognition of the emotional aspects of data practices and their implications on data-based knowledge and datafication processes in healthcare.
Citation: Big Data & Society
PubDate: 2022-05-11T07:14:40Z
DOI: 10.1177/20539517221098413
Issue No: Vol. 9, No. 1 (2022)
- In data we (don't) trust: The public adrift in data-driven public opinion
models
Authors: Slavko Splichal
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
This article seeks to address current debates comparing polls and opinion mining as empirically based figuration models of public opinion in the light of in-depth intellectual debates on the role and nature of public opinion that began after the French Revolution and the controversy over public opinion spurred by the invention of polls. Issues of historical quantification and re-conceptualisation of public opinion are addressed in four parts. The first summarises the history of the rise and fall of the concept of public opinion. The second re-examines the key controversies in the debates on the theoretical, empirical and social implications and consequences of the invention of polling. The third part scrutinises the datafication of public opinion that started with polling industry and continues in the age of big data and data mining. The final section discusses the controversial potentials of opinion-mining technology and suggests ways in which social scientists could critically respond to the big data and opinion-mining challenges in order to reintegrate the ideas of publicness, the public and public sphere into public opinion research.
Citation: Big Data & Society
PubDate: 2022-05-11T07:14:39Z
DOI: 10.1177/20539517221097319
Issue No: Vol. 9, No. 1 (2022)
- Carceral algorithms and the history of control: An analysis of the
Pennsylvania additive classification tool
Authors: Vanessa A. Massaro, Swarup Dhar, Darakhshan Mir, Nathan C. Ryan
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we term “carceral algorithms” that are used during incarceration. This paper is focused on the Pennsylvania Additive Classification Tool (PACT) used to classify prisoners’ custody levels while they are incarcerated. Algorithms that are used during incarceration warrant deeper attention by scholars because they have the power to enact the lived reality of the prisoner. The algorithm in this case determines the likelihood a person would endure additional disciplinary actions, can complete required programming, and gain experiences that, among other things, are distilled into variables feeding into the parole algorithm. Given such power, examining algorithms used on people currently incarcerated offers a unique analytic view to think about the dialectic relationship between data and algorithms. Our examination of the PACT is two-fold and complementary. First, our qualitative overview of the historical context surrounding PACT reveals that it is designed to prioritize incapacitation and control over rehabilitation. While it closely informs prisoner rehabilitation plans and parole considerations, it is rooted in population management for prison securitization. Second, on analyzing data for 146,793 incarcerated people in PA, along with associated metadata related to the PACT, we find it is replete with racial bias as well as errors, omissions, and inaccuracies. Our findings to date further caution against data-driven criminal justice reforms that rely on pre-existing data infrastructures and expansive, uncritical, data-collection routines.
Citation: Big Data & Society
PubDate: 2022-05-11T07:14:23Z
DOI: 10.1177/20539517221094002
Issue No: Vol. 9, No. 1 (2022)
- Artificial intelligence ethics by design. Evaluating public perception on
the importance of ethical design principles of artificial intelligence
Authors: Kimon Kieslich, Birte Keller, Christopher Starke
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Despite the immense societal importance of ethically designing artificial intelligence, little research on the public perceptions of ethical artificial intelligence principles exists. This becomes even more striking when considering that ethical artificial intelligence development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles (explainability, fairness, security, accountability, accuracy, privacy, and machine autonomy) are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even impossible, as developers must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case—the use of artificial intelligence in tax fraud detection. The results of a large conjoint survey ([math]) suggest that, by and large, German respondents evaluate the ethical principles as equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred ethical principles but also in the importance levels of the principles themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on artificial intelligence. Societal implications, as well as design challenges, are discussed.
Citation: Big Data & Society
PubDate: 2022-05-10T08:19:51Z
DOI: 10.1177/20539517221092956
Issue No: Vol. 9, No. 1 (2022)
- The material consequences of “chipification”: The case of
software-embedded cars
Authors: MC Forelle
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Today's modern car is an assemblage of mechanical and digital components, of metal panels that comprise its structure and silicon chips that run its functions. Communication and information studies scholars have interrogated the problematic aspects of the programs that run those functions, revealing serious issues surrounding privacy and security, worker surveillance, and racial, gendered, and class-based bias. This article contributes to that work by taking a step back and asking about the issues inherent not in the software running on these chips, but on the microchips themselves. Using the lens of “chipification”—the process by which a device is rendered capable of reading and processing data through the embedding of microchips—this article explores how the integration of microchips also involves processes that are often borne from, replicate, or create troubling power dynamics of their own. It takes light-duty passenger vehicles as a case study into how chipification is radically reshaping such processes as resource allocation, labor flows, and cultural practices around car manufacture, use, repair, and modification. By naming the process of chipification, this article allows researchers to identify and analyze the ways that integrating data processing capabilities into everyday devices is not a frictionless practice, but rather one that implicates a variety of power dynamics within these massive industries.
Citation: Big Data & Society
PubDate: 2022-05-03T11:17:00Z
DOI: 10.1177/20539517221095429
Issue No: Vol. 9, No. 1 (2022)
- Individual benefits and collective challenges: Experts’ views on
data-driven approaches in medical research and healthcare in the German
context
Authors: Lorina Buhr, Silke Schicktanz
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, informatics and humanities of digitalisation. The aim of the study was to explore expert opinions regarding current challenges and opportunities related to data-driven medicine and medical research and to provide a methodological framework for empirically grounded, continuous normative reflection. To this end, we developed a heuristic tool to map and structure empirical findings for normative analysis. Using this tool, our interview material points to a polarisation between individualistic and collectivistic orientated argumentations among experts. The study shows that a multilevel analysis is required to deal with complex normative implications of data-driven approaches in medical research and healthcare.
Citation: Big Data & Society
PubDate: 2022-04-26T07:21:41Z
DOI: 10.1177/20539517221092653
Issue No: Vol. 9, No. 1 (2022)
- Linguistic justice as a framework for designing, developing, and managing
natural language processing tools
Authors: Julia Nee, Genevieve Macfarlane Smith, Alicia Sheares, Ishita Rustagi
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
As natural language processing tools powered by big data become increasingly ubiquitous, questions of how to design, develop, and manage these tools and their impacts on diverse populations are pressing. We propose utilizing the concept of linguistic justice—the realization of equitable access to social and political life regardless of language—to provide a framework for examining natural language processing tools that learn from and use human language data. To support linguistic justice, we argue that natural language processing tools (along with the datasets that are used to train and evaluate them) must be examined not only from the perspective of a privileged, majority language user, but also from the perspectives of minoritized language users. Considering such perspectives can help to surface areas in which the data used within natural language processing tools may be (often inadvertently) working against linguistic justice by failing to provide access to information, services, or opportunities in users’ language of choice, underperforming for certain linguistic groups, or advancing harmful stereotypes that can lead to negative life outcomes for members of marginalized groups. At the same time, this framework can help to illuminate ways that these shortcomings can be addressed and allow us to use inclusive language data and approaches to leverage natural language processing technologies that advance linguistic justice.
Citation: Big Data & Society
PubDate: 2022-04-26T07:21:32Z
DOI: 10.1177/20539517221090930
Issue No: Vol. 9, No. 1 (2022)
- Datafication and the practice of intelligence production
Authors: Janet Chan, Carrie Sanders, Lyria Bennett Moses, Holly Blackmore
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Datafication of social life affects what society regards as knowledge. Jasanoff’s regimes of sight framework provides three ideal-type models of authorised knowing in environmental data practice. This paper applies Jasanoff's framework for analysing intelligence practice through an exploratory empirical study of crime and intelligence practitioners in a selection of police services in Australia, New Zealand, Canada and the United States. The paper argues that the ‘view from somewhere’ (VFS) captures the essence of existing police intelligence practices in the four countries but the ‘view from nowhere’ (VFN) is emerging as a possible future for police intelligence – an approach promoted by technology companies and supported mainly by police leaders and managers. The paper investigates the challenges and limits of a shift by police from VFS to VFN in the production of intelligence; the challenges are primarily political, which threaten the dominance of police contextual knowledge over ‘scientific’ knowledge. These political challenges also have symbolic and material implications. The paper concludes that, because of these challenges, a complete shift from VFS to VFN is not likely to happen. At best the two models might co-exist with the latter subordinate to the imperatives of the former, resulting in further tension between sworn officers and civilians, organisational inertia, as well as technologies that may be under-utilised or abandoned.
Citation: Big Data & Society
PubDate: 2022-04-25T05:33:11Z
DOI: 10.1177/20539517221089310
Issue No: Vol. 9, No. 1 (2022)
- Situating questions of data, power, and racial formation
Authors: Renee Shelby, Kathryn Henne
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
This special theme of Big Data & Society explores connections, relationships, and tensions that coalesce around data, power, and racial formation. This collection of articles and commentaries builds upon scholarly observations of data substantiating and transforming racial hierarchies. Contributors consider how racial projects intersect with interlocking systems of oppression across concerns of class, coloniality, dis/ability, gendered difference, and sexuality across contexts and jurisdictions. In doing so, this special issue illuminates how data can both reinforce and challenge colorblind ideologies as well as how data might be mobilized in support of anti-racist movements.
Citation: Big Data & Society
PubDate: 2022-04-25T05:32:48Z
DOI: 10.1177/20539517221090938
Issue No: Vol. 9, No. 1 (2022)
- Digital contact tracing in the pandemic cities: Problematizing the regime
of traceability in South Korea
Authors: Chamee Yang
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Since 2020, many countries worldwide have deployed digital contact tracing programs that rely on a range of digital sensors in the city to locate and map the routes of viral spread. Many critical commentaries have raised concerns about the privacy risks and trustworthiness of these programs. Extending these analyses, this paper opens up a different line of questioning that goes beyond privacy-centered single-axis critique of surveillance by considering digital contact tracing symptomatic of the broader changes in modes of urban governance that renders our cities traceable, knowable, and governable through data. Based on archival and real-time analysis of South Korean national and local COVID-19 dashboards, online forums, and interviews with South Korean public health practitioners, this paper offers a sociotechnical analysis of digital contact tracing that looks at the various intersections of state-political, bio-political, and techno-political power dynamics. In contrast to popular narratives that attributed the success of the Korean approach to digital contact tracing to its collectivist culture and smart city infrastructures, this paper suggests that the case can be better understood by looking at both the macro-level shift in the forms of governance that takes on a spatialized and networked character and the micro-level formation of moral responsibility that shape one's conduct as a health and safety-conscious citizens. As the latest realization of the expanding regime of traceability in digital/urban governance, the development of digital contract tracing is seen to parallel with concurrent changes occurring in multiple domains of life including knowledge production, cultural memory, and individual subjectivity.
Citation: Big Data & Society
PubDate: 2022-04-25T05:32:43Z
DOI: 10.1177/20539517221089294
Issue No: Vol. 9, No. 1 (2022)
- Understanding ‘passivity’ in digital health through imaginaries and
experiences of coronavirus disease 2019 contact tracing apps
Authors: Alessia Costa, Richard Milne
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Growing interest is being directed to the health applications of so-called ‘passive data’ collected through wearables and sensors without active input by users. High promises are attached to passive data and their potential to unlock new insights into health and illness, but as researchers and commentators have noted, this mode of data gathering also raises fundamental questions regarding the subject's agency, autonomy and privacy. To explore how these tensions are negotiated in practice, we present and discuss findings from an interview study with 30 members of the public in the UK and Italy, which examined their views and experiences of the coronavirus disease 2019 contact tracing apps as a large-scale, high-impact example of digital health technology using passive data. We argue that, contrary to what the phrasing ‘passive data’ suggests, passivity is not a quality of specific modes of data collection but is contingent on the very practices that the technology is supposed to unobtrusively capture.
Citation: Big Data & Society
PubDate: 2022-04-21T04:45:38Z
DOI: 10.1177/20539517221091138
Issue No: Vol. 9, No. 1 (2022)
- In search of the citizen in the datafication of public administration
Authors: Heather Broomfield, Lisa Reutter
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
The administrative reform of the datafied public administration places great emphasis on the classification, control, and prediction of citizen behavior and therefore has the potential to significantly impact citizen–state relations. There is a growing body of literature on data-oriented activism which aims to resist and counteract existing harmful data practices. However, little is known about the processes, policies, and political-economic structures that make datafication possible. There is a distinct research gap on situated and context-specific empirical research, which critically interrogates the premises, interests, and agendas of data-driven public administration and how stakeholders can impact them. This paper therefore studies the conditions of participation in public administration datafication. It asks the overall research question of how citizens are problematized and included in policy and practitioner discourse in the datafication of public administration. The paper takes Norway as its case and applies Cardullo and Kitchin’s scaffold of smart citizen participation at the system level. It makes use of a unique empirical insight into the field, consisting of a survey, interviews, and an extensive document analysis. Unexpectedly, we find that citizens and civil society are rarely engaged in this administrative reform. Instead, we identify a paternalistic, top-down, technocratic approach where the context, values, and agendas of datafication are obscured from the citizen.
Citation: Big Data & Society
PubDate: 2022-04-06T07:06:48Z
DOI: 10.1177/20539517221089302
Issue No: Vol. 9, No. 1 (2022)
- Co-designing algorithms for governance: Ensuring responsible and
accountable algorithmic management of refugee camp supplies
Authors: Rianne Dekker, Paul Koot, S. Ilker Birbil, Mark van Embden Andres
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and society is another means to achieve responsible and accountable algorithms that is largely overlooked in the literature. We present a case study of the development of an algorithmic tool to estimate the populations of refugee camps to manage the delivery of emergency supplies. This case study demonstrates how in different stages of development of the tool—data selection and pre-processing, training of the algorithm and post-processing and adoption—inclusion of knowledge from the field led to changes to the algorithm. Co-design supported responsibility of the algorithm in the selection of big data sources and in preventing reinforcement of biases. It contributed to accountability of the algorithm by making the estimations transparent and explicable to its users. They were able to use the tool for fitting purposes and used their discretion in the interpretation of the results. It is yet unclear whether this eventually led to better servicing of refugee camps.
Citation: Big Data & Society
PubDate: 2022-04-06T05:55:02Z
DOI: 10.1177/20539517221087855
Issue No: Vol. 9, No. 1 (2022)
- Diversity in sociotechnical machine learning systems
Authors: Sina Fazelpour, Maria De-Arteaga
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform how we measure diversity in a given context. Similarly, the lack of specificity about the precise mechanisms underpinning diversity’s potential benefits can result in uninformative generalities, invalid experimental designs, and illicit interpretations of findings. In this work, we draw on research in philosophy, psychology, and social and organizational sciences to make three contributions: First, we introduce a taxonomy of different diversity concepts from philosophy of science, and explicate the distinct epistemic and political rationales underlying these concepts. Second, we provide an overview of mechanisms by which diversity can benefit group performance. Third, we situate these taxonomies of concepts and mechanisms in the lifecycle of sociotechnical machine learning systems and make a case for their usefulness in fair and accountable machine learning. We do so by illustrating how they clarify the discourse around diversity in the context of machine learning systems, promote the formulation of more precise research questions about diversity’s impact, and provide conceptual tools to further advance research and practice.
Citation: Big Data & Society
PubDate: 2022-03-30T06:08:50Z
DOI: 10.1177/20539517221082027
Issue No: Vol. 9, No. 1 (2022)
- Utopia of abstraction: Digital organizations and the promise of
sovereignty
Authors: Tim Corballis, Max Soar
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Digital organizations form part of the new wave of blockchain technologies, following Bitcoin and related cryptocurrencies. “Utopia of ion” offers an analysis of the utopian promise of digital organizations through a reading of one such project, Colony. We provide a critique of the ideology of Colony's white paper, supplemented by readings of pages from its website, as a member of a genre of texts that promote their products through seemingly neutral, technical descriptions. Colony's texts suggest an abstract, contextless and scaleless organizational solution—powered by smart contracts on a blockchain—that, according to its proponents, might be applied to any social situation, from small firm to state-level governance. For its users, this organization combines a promise of sovereignty removed from that of the state, as well as implied financial returns. Our reading of Colony echoes the critiques of scholars arguing that cyberlibertarianism is a dominant politic of blockchain technologies. Furthermore, drawing on critiques of code as law and the elision of the social in smart contracts, we argue that Colony's vision presents a model of technical organization that substitutes for the state in the context of waning popular sovereignty. We ultimately suggest an understanding of digital organizations reminiscent of the settler colonial situation: the assumption of an empty social space to be filled, and the promise of sovereignty and riches for those occupying it. Analysis of these logics is relevant as hype increases around non-fungible tokens, Web3, and the corporate metaverse as well as data practices more widely.
Citation: Big Data & Society
PubDate: 2022-03-25T05:12:37Z
DOI: 10.1177/20539517221084587
Issue No: Vol. 9, No. 1 (2022)
- Computational grounded theory revisited: From computer-led to
computer-assisted text analysis
Authors: Hjalmar Bang Carlsen, Snorre Ralund
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
The size and variation in both meaning-making and populations that characterize much contemporary text data demand research processes that support both discovery, interpretation and measurement. We assess one dominant strategy within the social sciences that takes a computer-led approach to text analysis. The approach is coined computational grounded theory. This strategy, we argue, relies on a set of unwarranted assumptions, namely, that unsupervised models return natural clusters of meaning, that the researcher can understand text with limited immersion and that indirect validation is sufficient for ensuring unbiased and precise measurement. In response to this criticism, we develop a framework that is computer assisted. We argue that our reformulation of computational grounded theory better aligns with the principles within grounded theory, anthropological theory generation and ethnography.
Citation: Big Data & Society
PubDate: 2022-03-16T08:28:20Z
DOI: 10.1177/20539517221080146
Issue No: Vol. 9, No. 1 (2022)
- Ideological variation in preferred content and source credibility on
Reddit during the COVID-19 pandemic
Authors: Wallace Chipidza, Christopher Krewson, Nicole Gatto, Elmira Akbaripourdibazar, Tendai Gwanzura
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
In this exploratory study, we examine political polarization regarding the online discussion of the COVID-19 pandemic. We use data from Reddit to explore the differences in the topics emphasized by different subreddits according to political ideology. We also examine whether there are systematic differences in the credibility of sources shared by the subscribers of subreddits that vary by ideology, and in the tendency to share information from sources implicated in spreading COVID-19 misinformation. Our results show polarization in topics of discussion: the Trump, White House, and economic relief topics are statistically more prominent in liberal subreddits, and China and deaths topics are more prominent in conservative subreddits. There are also significant differences between liberal and conservative subreddits in their preferences for news sources. Liberal subreddits share and discuss articles from more credible news sources than conservative subreddits, and conservative subreddits are more likely than liberal subreddits to share articles from sites flagged for publishing COVID-19 misinformation.
Citation: Big Data & Society
PubDate: 2022-03-09T10:04:55Z
DOI: 10.1177/20539517221076486
Issue No: Vol. 9, No. 1 (2022)
- Political affiliation moderates subjective interpretations of COVID-19
graphs
Authors: Jonathan D Ericson, William S Albert, Ja-Nae Duane
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
We examined the relationship between political affiliation, perceptual (percentage, slope) estimates, and subjective judgements of disease prevalence and mortality across three chart types. An online survey (N = 787) exposed separate groups of participants to charts displaying (a) COVID-19 data or (b) COVID-19 data labeled ‘Influenza (Flu)’. Block 1 examined responses to cross-sectional mortality data (bar graphs, treemaps); results revealed that perceptual estimates comparing mortality in two countries were similar across political affiliations and chart types (all ps > .05), while subjective judgements revealed a disease x political party interaction (p
Citation: Big Data & Society
PubDate: 2022-03-04T01:19:51Z
DOI: 10.1177/20539517221080678
Issue No: Vol. 9, No. 1 (2022)
- Corrigendum to Machine Anthropology: A View from International Relations
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Citation: Big Data & Society
PubDate: 2022-02-24T03:31:41Z
DOI: 10.1177/20539517221080835
Issue No: Vol. 9, No. 1 (2022)
- Corrigendum to Low on trust, high on use: Datafied media, trust and
everyday life
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Citation: Big Data & Society
PubDate: 2022-02-24T03:30:56Z
DOI: 10.1177/20539517221080834
Issue No: Vol. 9, No. 1 (2022)
- Influence government: Exploring practices, ethics, and power in the use of
targeted advertising by the UK state
Authors: Ben Collier, Gemma Flynn, James Stewart, Daniel Thomas
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
We have identified an emerging tool being used by the UK government across a range of public bodies in the service of public policy - the online targeted advertising infrastructure and the practices, consultancy firms, and forms of expertise which have grown up around it. This reflects an intensification and adaptation of a broader ‘behavioural turn’ in the governmentality of the UK state and the increasing sophistication of everyday government communications. Contemporary UK public policy is fusing with the powerful tools for behaviour change created by the platform economy. Operational data and associated systems of classification and profiling from public bodies are being hybridised with traditional consumer marketing profiles and then ‘projected’ onto the classification systems of the targeted advertising infrastructures. This is not simply a case of algorithms being used for sorting, surveilling, and scoring; rather this suggests that targeted interventions in the cultural and behavioural life of communities are now a core part of governmental power which is being algorithmically-driven, in combination with influencer networks, traditional forms of messaging, and frontline operational practices. We map these uses and practices of what we describe as the ‘Surveillance Influence Infrastructure’, identifying key ethical issues and implications which we believe have yet to be fully investigated or considered. What we find particularly striking is the coming-together of two separate structures of power - the governmental turn to behaviourism and prevention on one hand, and the infrastructures of targeting and influence (and their complex tertiary markets) on the other. We theorise this as a move beyond ‘nudge’ or ‘behavioural science’ approaches, towards a programme which we term ‘influence government’.
Citation: Big Data & Society
PubDate: 2022-02-24T03:30:37Z
DOI: 10.1177/20539517221078756
Issue No: Vol. 9, No. 1 (2022)
- Grøn Genstart: A quali-quantitative micro-history of a political idea
in real-time
Authors: Annika SH Isfeldt, Thyge R Enggaard, Anders Blok, Morten A Pedersen
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
In this study, we build on a recent social data scientific mapping of Danish environmentalist organizations and activists during the COVID-19 lockdown in order to sketch a distinct genre of digital social research that we dub a quali-quantitative micro-history of ideas in real-time. We define and exemplify this genre by tracing and tracking the single political idea and activist slogan of grøn genstart (‘green restart’) across Twitter and other public–political domains. Specifically, we achieve our micro-history through an iterative and mutual attuning between computational and netnographic registers and techniques, in ways that contribute to the nascent field of computational anthropology. By documenting the serial ways in and different steps through which our inquiry was continually fed and enhanced by crossing over from (n)ethnographic observation to computational exploration, and vice versa, we offer up our grøn genstart case account as exemplary of wider possibilities in this line of inquiry. In particular, we position the genre of micro-history of ideas in real-time within the increasingly wide and heterogeneous space of digital social research writ large, including its established concerns with ‘big and broad’ social data, the repurposing of computational ‘interface’ techniques for socio-cultural research, as well as diverse aspirations for deploying digital data within novel combinations of qualitative and quantitative methods.
Citation: Big Data & Society
PubDate: 2022-02-24T03:30:17Z
DOI: 10.1177/20539517211070300
Issue No: Vol. 9, No. 1 (2022)
- ‘The interface of the future’: Mixed reality, intimate data
and imagined temporalities
Authors: Ben Egliston, Marcus Carter
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
This article examines discourses about mixed reality as a data-rich sensing technology – specifically, engaging with discourses of time as framed by developers, engineers and in corporate PR and marketing in a range of public facing materials. We focus on four main settings in which mixed reality is imagined to be used, and in which time was a dominant discursive theme – (1) the development of mixed reality by big tech companies, (2) the use of mixed reality for defence, (3) mixed reality as a technology for control of populations in civil society and (4) mixed reality as a technology used in workplace settings. Across these settings, the broad narrative is that mixed reality technologies afford overwhelmingly positive benefits like efficiency and security through their capture, relay and rendition of data (about the environment, about the body etc.) – affording a form of anticipatory power to the user. The framing of temporality, we argue, is underlain by social and political values, which represent certain interests, but leave others out in the imagination of mixed reality's technological advance.
Citation: Big Data & Society
PubDate: 2022-02-24T03:29:38Z
DOI: 10.1177/20539517211063689
Issue No: Vol. 9, No. 1 (2022)
- Justice, injustice, and artificial intelligence: Lessons from political
theory and philosophy
Authors: Lucia M. Rafanelli
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Some recent uses of artificial intelligence for (for example) facial recognition, evaluating resumes, and sorting photographs by subject matter have revealed troubling disparities in performance or impact based on the demographic traits (like race and gender) of subject populations. These disparities raise pressing questions about how using artificial intelligence can work to promote justice or entrench injustice. Political theorists and philosophers have developed nuanced vocabularies and theoretical frameworks for understanding and adjudicating disputes about what justice requires and what constitutes injustice. The interdisciplinary community committed to understanding and conscientiously using big data could benefit from this work. Thus, in the spirit of encouraging cross-disciplinary dialogue and collaboration, this piece examines contemporary scholarship in political theory and philosophy to illustrate some of the vocabularies and frameworks political theorists and philosophers have developed for thinking about justice and injustice. It then draws on these frameworks to illuminate how the use of artificial intelligence can implicate questions of justice, with a focus on institutional discrimination, structural injustice, and epistemic injustice. Ultimately, the piece argues that the use of artificial intelligence—far from representing a decision to take power out of human hands—represents a novel way of harnessing human power, making questions of justice central to its conscientious undertaking.
Citation: Big Data & Society
PubDate: 2022-02-23T01:36:30Z
DOI: 10.1177/20539517221080676
Issue No: Vol. 9, No. 1 (2022)
- Organizing an “organizationless” protest campaign in the
WeChatsphere
Authors: Hao Cao
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
The introduction of digital technologies in collective actions seems to have transformed the dynamics of movement organizing and enabled divergent forms of protest organizing. While some studies emphasize “organizationless” organizing in which traditional organizational forms—social movements organizations and formal-bureaucratic structures—have been pushed into the margins, other studies showcase how traditional forms have assumed alternative features, for example, connective leadership and organizations with fluid boundaries. While existing research correctly points out the evolving organizing dynamics and forms in digital activism, few studies have accounted for why digitally enabled protests take certain organizing forms over others among multiple modes of interaction between protesters and digital technologies. Using a case study of a protest campaign organized by Chinese American immigrants, this study illustrates why immigrant activists struggled to keep the campaign “organizationless” on WeChat, a China-based digital platform that afforded other forms of organizing over such an organizing mode. Building on the mechanism-based approach in social movement studies, the findings show that immigrant activists’ emotional–cognitive responses to the changing digital environments became the driving force behind the relational choices to maintain the protest “organizationless.” The study, therefore, may not only inform future studies to explore why certain structures of protest networks emerge and develop but also contribute to the mechanism-based approach by foregrounding emotional–cognitive mechanisms, which mediate environmental and relational mechanisms.
Citation: Big Data & Society
PubDate: 2022-02-23T01:36:00Z
DOI: 10.1177/20539517221078823
Issue No: Vol. 9, No. 1 (2022)
- The data will not save us: Afropessimism and racial antimatter in the
COVID-19 pandemic
Authors: Anthony Ryan Hatch
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
The Trump Administration's governance of COVID-19 racial health disparities data has become a key front in the viral war against the pandemic and racial health injustice. In this paper, I analyze how the COVID-19 pandemic joins an already ongoing racial spectacle and system of structural gaslighting organized around “racial health disparities” in the United States and globally. The field of racial health disparities has yet to question the domain assumptions that uphold its field of investigation; as a result, the entire reform program called for by racial health disparities science is already featured on the menu of the white supremacist power structure. The societal infrastructure that produces scientific knowledge about patterns of health and disease in the human population needs to confront its structural position as part of the racial spectacle organized around racial health disparities in the United States. This paper offers an interpretation of racial antimatter to explain why the data will not save us in the COVID-19 pandemic, drawing on articulations of racial spectacle and structural gaslighting within critical race theory and Afropessimist thought. By positioning events in the COVID-19 pandemic together within the same racially speculative frame, I show how the collection of racial health disparities data came up against white supremacists’ political ambitions in a time-space where the demand for human life to matter and the iterative regeneration of racial antimatter collided. This paper highlights the need for ongoing analysis of the unfolding and future spectacles organized around racial health disparities.
Citation: Big Data & Society
PubDate: 2022-02-23T01:35:42Z
DOI: 10.1177/20539517211067948
Issue No: Vol. 9, No. 1 (2022)
- Public views of the smart city: Towards the construction of a social
problem
Authors: Emiel A. Rijshouwer, Els M. Leclercq, Liesbet van Zoonen
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Digitization and datafication of public space have a significant impact on how cities are developed, governed, perceived and used. As technological developments are based upon political decisions, which impact people’s everyday lives, and from which not everyone benefits or suffers equally, we argue that ‘the smart city’ should be part of continuous public debate; that it should be considered and treated as a social problem. Through nine focus groups, we invited respondents to explore and discuss instances and dilemmas of the smart city. We investigated which interpretative repertoires they used to frame the smart city as a social and actionable problem. Following Blumer's and Gamson's theories on the social construction of problems and on collective action frames, we assessed respondents’ discursive interpretations and their subjective construction of their senses of injustice, agency and identity regarding this subject. We find that – in the context of the city of Rotterdam in The Netherlands – citizens do not experience and consider the smart city as a social and actionable problem. Although they do associate the technological development of smart cities with potential threats, this does not change or constrain their sense of ‘actionability’, nor their behaviour, as they consider themselves to be powerless individuals regarding what, in their eyes, is a complex, elusive and inevitable situation they are confronted with. Strikingly, rather than specifically and contextually reflecting on smart city issues, respondents tended to express their concerns in the more general context of digital and data technologies invading everyday life.
Citation: Big Data & Society
PubDate: 2022-02-09T04:07:16Z
DOI: 10.1177/20539517211072190
Issue No: Vol. 9, No. 1 (2022)
- Ethnographic data in the age of big data: How to compare and combine
Authors: Andreas Bjerre-Nielsen, Kristoffer Lind Glavind
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Big data enables researchers to closely follow the behavior of large groups of individuals by using high-frequency digital traces. However, these digital traces often lack context, and it is not always clear what is measured. In contrast, data from ethnographic fieldwork follows a limited number of individuals but can provide the context often lacking from big data. Yet, there is an under-explored potential in combining ethnographic data with big data and other digital data sources. This paper presents ways that quantitative research designs can combine big data and ethnographic data and account for the synergies that such combinations can provide. We highlight the differences and similarities between ethnographic data and big data, focusing on the three dimensions: individuals, depth of information, and time. We outline how ethnographic data can validate big data by providing a “ground truth” and complement it by giving a “thick description.” Further, we lay out ways that analysis carried out using big data could benefit from collaboration with ethnographers, and we discuss the potential within the fields of machine learning and causal inference.
Citation: Big Data & Society
PubDate: 2022-02-09T04:07:03Z
DOI: 10.1177/20539517211069893
Issue No: Vol. 9, No. 1 (2022)
- Capitalizing on transparency: Commercial surveillance and pharmaceutical
marketing after the Physician Sunshine Act
Authors: Shai Mulinari, Piotr Ozieranski
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
How corporations surveil and influence consumers using big data tools is a major area of research and public debate. However, few studies explore it in relation to physicians in the USA, even though they have been surveilled and targeted by the pharmaceutical industry since at least the 1950s. Indeed, in 2010, concerns about the pharmaceutical industry's undue influence led to the passing of the Physician Sunshine Act, a unique piece of transparency legislation that requires companies to report their financial ties to physicians and teaching hospitals in a public database. This article argues that while the Sunshine Act has clearly helped expose important commercial influences on both prescribing and the scale of industry involvement with physicians, it has also, paradoxically, fuelled further commercial surveillance and marketing. The article casts new light on innovative pharmaceutical marketing approaches and the key role of data brokers and analytics companies in the identification, targeting, managing, and surveillance of physicians. We place this analysis within the political economies of the pharmaceutical industry, surveillance-based marketing, and transparency, and argue that policies to promote increased transparency must be tightly tied to policies that impede the commodification and use of transparency data for surveillance and marketing purposes.
Citation: Big Data & Society
PubDate: 2022-02-09T04:07:00Z
DOI: 10.1177/20539517211069631
Issue No: Vol. 9, No. 1 (2022)
- Computational ethnography: A view from sociology
Authors: Phillip Brooker
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
This commentary elaborates on the ideas and projects outlined in this special issue, from a specifically sociological perspective. Much recent work in sociology proposes ‘methods mashups’ of ethnography and digital data/computational tools in different and diverse ways. However, typically, these have taken the form of applying (with or without tweaks) the principles of ethnography to new domains and data types, as if ethnography itself is stable and immutable; that it has a universal set of methodological principles that unify ethnographic practice. Returning to anthropology (whence, arguably, ethnography originally came) is, therefore, a useful way to extend our methodological thinking to (re)consider what ethnography is and how it operates, and from there think more clearly about how it may be effectively combined with digital data/computational tools in an emerging ‘Computational Anthropology’.
Citation: Big Data & Society
PubDate: 2022-01-24T12:12:40Z
DOI: 10.1177/20539517211069892
Issue No: Vol. 9, No. 1 (2022)
- Taking stock of COVID-19 health status certificates: Legal implications
for data privacy and human rights
Authors: Ana Beduschi
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
The technological solutions adopted during the current pandemic will have a lasting impact on our societies. Currently, COVID-19 health status certificates are being deployed around the world, including in Europe, the United States and China. When combined with identity verification, these digital and paper-based certificates allow individuals to prove their health status by showing recent COVID-19 tests results, full vaccination records or evidence of recovery from COVID-19. Most countries in the Global South, where vaccination rates are low, have not yet fully implemented such certificates, although several initiatives are currently underway. That is, for instance, the case in the African Union. Yet, it is not sufficient to develop technical solutions for the verification of COVID-19 health status. Because technologies do not evolve in a legal vacuum, the existing laws and regulations must be respected. The risks of implementing such technologies must be anticipated and mitigated as much as possible before any large-scale deployment. Risk mitigation should also underpin strategies throughout the deployment of these certificates. This article evaluates the key legal implications of COVID-19 health status certificates for data privacy and human rights. In doing so, it contributes to the current debates, thus informing policymakers in this area of vital national and international interest
Citation: Big Data & Society
PubDate: 2022-01-24T03:26:45Z
DOI: 10.1177/20539517211069300
Issue No: Vol. 9, No. 1 (2022)
- Alternative data and sentiment analysis: Prospecting non-standard data in
machine learning-driven finance
Authors: Kristian Bondo Hansen, Christian Borch
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of (a) prospecting (i.e. rendering such data amenable to processing with the aid of analytics tools) and (b) assetization (i.e. the transformation of data into tradable assets). We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues.
Citation: Big Data & Society
PubDate: 2022-01-20T12:57:56Z
DOI: 10.1177/20539517211070701
Issue No: Vol. 9, No. 1 (2022)
- The Thick Machine: Anthropological AI between explanation and explication
Authors: Anders Kristian Munk, Asger Gehrt Olesen, Mathieu Jacomy
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
According to Clifford Geertz, the purpose of anthropology is not to explain culture but to explicate it. That should cause us to rethink our relationship with machine learning. It is, we contend, perfectly possible that machine learning algorithms, which are unable to explain, and could even be unexplainable themselves, can still be of critical use in a process of explication. Thus, we report on an experiment with anthropological AI. From a dataset of 175K Facebook comments, we trained a neural network to predict the emoji reaction associated with a comment and asked a group of human players to compete against the machine. We show that a) the machine can reach the same (poor) accuracy as the players (51%), b) it fails in roughly the same ways as the players, and c) easily predictable emoji reactions tend to reflect unambiguous situations where interpretation is easy. We therefore repurpose the failures of the neural network to point us to deeper and more ambiguous situations where interpretation is hard and explication becomes both necessary and interesting. We use this experiment as a point of departure for discussing how experiences from anthropology, and in particular the tension between formalist ethnoscience and interpretive thick description, might contribute to debates about explainable AI.
Citation: Big Data & Society
PubDate: 2022-01-18T11:31:45Z
DOI: 10.1177/20539517211069891
Issue No: Vol. 9, No. 1 (2022)
- Why Personal Dreams Matter: How professionals affectively engage with the
promises surrounding data-driven healthcare in Europe
Authors: Marthe Stevens, Rik Wehrens, Johanna Kostenzer, Anne Marie Weggelaar-Jansen, Antoinette de Bont
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals (including legal, governance and ethical experts, healthcare staff and data scientists) between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences' We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare.
Citation: Big Data & Society
PubDate: 2022-01-11T10:32:01Z
DOI: 10.1177/20539517211070698
Issue No: Vol. 9, No. 1 (2022)
- Digital phenotyping and the (data) shadow of Alzheimer's disease
Authors: Richard Milne, Alessia Costa, Natassia Brenman
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
In this paper, we examine the practice and promises of digital phenotyping. We build on work on the ‘data self’ to focus on a medical domain in which the value and nature of knowledge and relations with data have been played out with particular persistence, that of Alzheimer's disease research. Drawing on research with researchers and developers, we consider the intersection of hopes and concerns related to both digital tools and Alzheimer's disease using the metaphor of the ‘data shadow’. We suggest that as a tool for engaging with the nature of the data self, the shadow is usefully able to capture both the dynamic and distorted nature of data representations, and the unease and concern associated with encounters between individuals or groups and data about them. We then consider what the data shadow ‘is’ in relation to ageing data subjects, and the nature of the representation of the individual's cognitive state and dementia risk that is produced by digital tools. Second, we consider what the data shadow ‘does’, through researchers and practitioners’ discussions of digital phenotyping practices in the dementia field as alternately empowering, enabling and threatening.
Citation: Big Data & Society
PubDate: 2022-01-11T10:31:31Z
DOI: 10.1177/20539517211070748
Issue No: Vol. 9, No. 1 (2022)
- Digital epidemiology, deep phenotyping and the enduring fantasy of
pathological omniscience
Authors: Lukas Engelmann
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future.
Citation: Big Data & Society
PubDate: 2022-01-11T10:30:12Z
DOI: 10.1177/20539517211066451
Issue No: Vol. 9, No. 1 (2022)
- ‘What about the dads'’ Linking fathers and children in
administrative data: A systematic scoping review
Authors: Irina Lut, Katie Harron, Pia Hardelid, Margaret O’Brien, Jenny Woodman
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
Research has shown that paternal involvement positively impacts on child health and development. We aimed to develop a conceptual model of dimensions of fatherhood, identify and categorise methods used for linking fathers with their children in administrative data, and map these methods onto the dimensions of fatherhood. We carried out a systematic scoping review to create a conceptual framework of paternal involvement and identify studies exploring the impact of paternal exposures on child health and development outcomes using administrative data. We identified four methods that have been used globally to link fathers and children in administrative data based on family or household identifiers using address data, identifiable information about the father on the child's birth registration, health claims data, and Personal Identification Numbers. We did not identify direct measures of paternal involvement but mapping linkage methods to the framework highlighted possible proxies. The addition of paternal National Health Service numbers to birth notifications presents a way forward in the advancement of fatherhood research using administrative data sources.
Citation: Big Data & Society
PubDate: 2022-01-11T03:08:37Z
DOI: 10.1177/20539517211069299
Issue No: Vol. 9, No. 1 (2022)
- The Chilling Effects of Digital Dataveillance: A Theoretical Model and an
Empirical Research Agenda
Authors: Moritz Büchi, Noemi Festic, Michael Latzer
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
People's sense of being subject to digital dataveillance can cause them to restrict their digital communication behavior. Such a chilling effect is essentially a form of self-censorship in everyday digital media use with the attendant risks of undermining individual autonomy and well-being. This article combines the existing theoretical and limited empirical work on surveillance and chilling effects across fields with an analysis of novel data toward a research agenda. The institutional practice of dataveillance—the automated, continuous, and unspecific collection, retention, and analysis of digital traces—affects individual behavior. A mechanism-based causal model based on the theory of planned behavior is proposed for the micro level: An individual's increased sense of dataveillance causes their subjective probability assigned to negative outcomes of digital communication behavior to increase and attitudes toward this communication to become less favorable, ultimately decreasing the intention to engage in it. In aggregate and triggered through successive salience shocks such as data scandals, dataveillance is accordingly hypothesized to lower the baseline of free digital communication in a society through the chilling effects mechanism. From the developed theoretical model, a set of methodological consequences and questions for future studies are derived.
Citation: Big Data & Society
PubDate: 2022-01-06T12:18:14Z
DOI: 10.1177/20539517211065368
Issue No: Vol. 9, No. 1 (2022)
- Consumers are willing to pay a price for explainable, but not for green
AI. Evidence from a choice-based conjoint analysis
Authors: Pascal D König, Stefan Wurster, Markus B Siewert
Abstract: Big Data & Society, Volume 9, Issue 1, January-June 2022.
A major challenge with the increasing use of Artificial Intelligence (AI) applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from a choice-based conjoint analysis with a sample of more than 1.000 respondents from Germany indicate that people hardly care about the energy efficiency of AI; and while they do value transparency through explainable AI, this added value of an application is offset by minor costs. The findings shed light on what kinds of AI people are likely to demand and have important implications for policy and regulation.
Citation: Big Data & Society
PubDate: 2022-01-04T03:42:11Z
DOI: 10.1177/20539517211069632
Issue No: Vol. 9, No. 1 (2022)
