Subjects -> SOCIAL SCIENCES (Total: 1816 journals)
    - BIRTH CONTROL (22 journals)
    - CHILDREN AND YOUTH (260 journals)
    - FOLKLORE (30 journals)
    - MATRIMONY (16 journals)
    - MEN'S INTERESTS (16 journals)
    - MEN'S STUDIES (96 journals)
    - SEXUALITY (57 journals)
    - SOCIAL SCIENCES (1092 journals)
    - WOMEN'S INTERESTS (44 journals)
    - WOMEN'S STUDIES (183 journals)

SOCIAL SCIENCES (1092 journals)                  1 2 3 4 5 6     

Showing 1 - 136 of 136 Journals sorted alphabetically
(En)clave Comahue. Revista Patagónica de Estudios Sociales     Open Access  
3C Empresa     Open Access   (Followers: 4)
A contrario     Full-text available via subscription   (Followers: 3)
AAS Open Research     Open Access   (Followers: 2)
Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi     Open Access   (Followers: 1)
Abant Kültürel Araştırmalar Dergisi     Open Access  
Abordajes : Revista de Ciencias Sociales y Humanas     Open Access   (Followers: 1)
Aboriginal and Islander Health Worker Journal     Full-text available via subscription   (Followers: 18)
About Performance     Full-text available via subscription   (Followers: 13)
Academic Journal of Interdisciplinary Studies     Open Access   (Followers: 1)
Academicus International Scientific Journal     Open Access   (Followers: 4)
Access     Full-text available via subscription   (Followers: 29)
ACCESS: Critical Perspectives on Communication, Cultural & Policy Studies     Full-text available via subscription   (Followers: 16)
ACCORD Occasional Paper     Open Access   (Followers: 4)
Accountability in Research: Policies and Quality Assurance     Hybrid Journal   (Followers: 18)
Acta Academica     Full-text available via subscription   (Followers: 6)
Acta Humana     Open Access   (Followers: 1)
Acta Scientiarum. Human and Social Sciences     Open Access   (Followers: 12)
Acta Universitatis Sapientiae, Philologica     Open Access   (Followers: 2)
Actes de la Journée des Sciences et Savoirs     Open Access   (Followers: 9)
Adelphi series     Hybrid Journal   (Followers: 14)
Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi     Open Access  
Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / Adiyaman University Journal of Social Sciences     Open Access  
Administrative Science Quarterly     Full-text available via subscription   (Followers: 246)
Administrative Theory & Praxis     Full-text available via subscription   (Followers: 8)
Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi     Open Access  
Adultspan Journal     Hybrid Journal   (Followers: 1)
Advocate: Newsletter of the National Tertiary Education Union     Full-text available via subscription   (Followers: 1)
África     Open Access   (Followers: 1)
Africa Spectrum     Open Access   (Followers: 17)
African Affairs     Hybrid Journal   (Followers: 73)
African Renaissance     Full-text available via subscription   (Followers: 4)
African Research Review     Open Access   (Followers: 9)
African Social Science Review     Open Access   (Followers: 11)
Afrika Focus     Open Access   (Followers: 1)
Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi     Open Access  
Ágora : revista de divulgação científica     Open Access  
Ágora de Heterodoxias     Open Access   (Followers: 1)
Ağrı İbrahim Çeçen Üniversitesi Sosyal Bilimler Enstitüsü Dergisi     Open Access   (Followers: 1)
Ahi Evran Üniversitesi Sosyal Bilimler Enstitüsü Dergisi     Open Access  
Akademik Bakış Uluslararası Hakemli Sosyal Bilimler Dergisi     Open Access   (Followers: 1)
Akademik Hassasiyetler     Open Access  
Akademik İncelemeler Dergisi     Open Access   (Followers: 1)
Akademika : Journal of Southeast Asia Social Sciences and Humanities     Open Access   (Followers: 8)
AKADEMOS     Open Access   (Followers: 3)
Al Farabi Uluslararası Sosyal Bilimler Dergisi     Open Access  
Al-Mabsut : Jurnal Studi Islam dan Sosial     Open Access   (Followers: 1)
AL-Qadissiya Magzine for Human Sciences     Open Access   (Followers: 3)
Aleph : UCLA Undergraduate Research Journal for the Humanities and Social Sciences     Open Access   (Followers: 8)
Aletheia : Revista de Desarrollo Humano, Educativo y Social Contemporáneo     Open Access   (Followers: 1)
Algarrobo-MEL     Open Access   (Followers: 11)
Alinteri Journal of Social Sciences     Open Access   (Followers: 1)
Alliage     Free  
Alteridade     Open Access  
Ambigua : Revista de Investigaciones sobre Género y Estudios Culturales     Open Access   (Followers: 1)
American Communist History     Hybrid Journal   (Followers: 20)
Anais do Congresso de Pesquisa e Extensão e da Semana de Ciências Sociais da UEMG/Barbacena     Open Access  
Anais Eletrônicos do Congresso Epistemologias do Sul     Open Access   (Followers: 1)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 4)
Anales de la Universidad de Chile     Open Access  
Análisis     Open Access  
Analysis     Full-text available via subscription   (Followers: 4)
Andamios. Revista de Investigacion Social     Open Access   (Followers: 1)
Anduli : Revista Andaluza de Ciencias Sociales     Open Access  
Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi     Open Access  
Anka E-Dergi     Open Access  
Ankara Üniversitesi Sosyal Bilimler Dergisi     Open Access  
Ankara University SBF Journal     Open Access   (Followers: 1)
Annals of Humanities and Development Studies     Open Access   (Followers: 7)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 49)
Annuaire de l’EHESS     Open Access  
Anthropocene Review     Hybrid Journal   (Followers: 8)
Anthurium : A Caribbean Studies Journal     Open Access   (Followers: 8)
Approches inductives : Travail intellectuel et construction des connaissances     Open Access  
Apuntes : Revista de Ciencias Sociales     Open Access   (Followers: 2)
Apuntes de Investigación del CECYP     Open Access  
Arbejdspapirer : Professionshøjskolen Metropol     Open Access   (Followers: 5)
Arbetsliv i omvandling     Open Access  
Arbor     Open Access  
Argomenti. Rivista di economia, cultura e ricerca sociale     Open Access   (Followers: 4)
Argumentos : Revista do Departamento de Ciências Sociais da Unimontes     Open Access  
Argumentos. Revista de crítica social     Open Access  
Around the Globe     Full-text available via subscription   (Followers: 1)
Arquivos do CMD : Cultura, Memória e Desenvolvimento     Open Access  
Articulo - Journal of Urban Research     Open Access   (Followers: 8)
Artvin Coruh University International Journal of Social Sciences     Open Access  
Asia Pacific Journal of Sport and Social Science     Hybrid Journal   (Followers: 6)
Asian Journal of German and European Studies     Open Access  
Asian Journal of Quality of Life     Open Access   (Followers: 1)
Asian Journal of Social Science     Hybrid Journal   (Followers: 18)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 8)
Asian Research Journal of Arts & Social Sciences     Open Access   (Followers: 1)
Asian Social Science     Open Access   (Followers: 8)
Astrolabio, Nueva Época     Open Access  
Asya Araştırmaları Uluslararasi Sosyal Bilimler Dergisi / Journal of Asian Studies     Open Access   (Followers: 1)
Atatürk Dergisi     Open Access  
Atatürk Üniversitesi Edebiyat Fakültesi Dergisi     Open Access  
Ateneo Chinese Studies Program Lecture Series     Open Access  
Aurum Journal of Social Sciences     Open Access  
Australasian Review of African Studies, The     Full-text available via subscription   (Followers: 2)
Australian Aboriginal Studies     Full-text available via subscription   (Followers: 9)
Australian and Aotearoa New Zealand Psychodrama Association Journal     Full-text available via subscription   (Followers: 2)
Australian Journal of Emergency Management     Full-text available via subscription   (Followers: 29)
Australian Journal on Volunteering     Full-text available via subscription   (Followers: 2)
Australian Population Studies     Open Access  
Austrian Journal of South-East Asian Studies     Open Access   (Followers: 1)
Bandung : Journal of the Global South     Open Access   (Followers: 1)
BARATARIA. Revista Castellano-Manchega de Ciencias sociales     Open Access  
Barn : Forskning om barn og barndom i Norden     Open Access   (Followers: 1)
Basic and Applied Social Psychology     Hybrid Journal   (Followers: 45)
Basic Income Studies     Hybrid Journal   (Followers: 8)
Bayero Journal of Pure and Applied Sciences     Open Access   (Followers: 3)
Behavioural Sciences Undergraduate Journal     Open Access   (Followers: 5)
Berkeley Undergraduate Journal     Full-text available via subscription   (Followers: 1)
Beykent Üniversitesi Sosyal Bilimler Dergisi     Open Access  
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Big Data & Society     Open Access   (Followers: 52)
Bildhaan : An International Journal of Somali Studies     Open Access   (Followers: 5)
Bilecik Şeyh Edebali University Journal of Social Science Institute     Open Access  
Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi     Open Access  
Black Sea Journal of Public and Social Science     Open Access  
Black Women, Gender & Families     Full-text available via subscription   (Followers: 18)
BMC Medical Ethics     Open Access   (Followers: 23)
Bodhi : An Interdisciplinary Journal     Open Access   (Followers: 3)
Body Image     Hybrid Journal   (Followers: 16)
BOGA : Basque Studies Consortium Journal     Open Access   (Followers: 3)
Boletín Cultural y Bibliográfico     Open Access   (Followers: 3)
Boletín Memoria     Open Access  
Border Crossing : Transnational Working Papers     Open Access   (Followers: 4)
Brain and Cognition     Hybrid Journal   (Followers: 38)
British Review of New Zealand Studies     Full-text available via subscription   (Followers: 4)
BU Academic Review     Open Access  
Bulletin de l’Institut Français d’Études Andines     Open Access   (Followers: 5)
Bulletin of Social Informatics Theory and Application     Open Access   (Followers: 1)
Búsqueda     Open Access  
Caderno CRH     Open Access   (Followers: 3)
Cadernos de Ciências Sociais Aplicadas     Open Access   (Followers: 1)
Cadernos de Estudos Sociais     Open Access   (Followers: 3)
Cadernos de Saúde     Open Access   (Followers: 1)
California Italian Studies Journal     Full-text available via subscription   (Followers: 6)
California Journal of Politics and Policy     Open Access   (Followers: 1)
Cambio : Rivista sulle Trasformazioni Sociali     Open Access  
Caminho Aberto : Revista de Extensão do IFSC     Open Access  
Campos en Ciencias Sociales     Open Access  
Canadian Journal of Human Sexuality     Hybrid Journal   (Followers: 3)
Canadian Social Science     Open Access   (Followers: 14)
Caradde : Jurnal Pengabdian Kepada Masyarakat     Open Access  
Caribbean Studies     Full-text available via subscription   (Followers: 12)
Castalia : Revista de Psicología de la Academia     Open Access  
Catalan Social Sciences Review     Open Access   (Followers: 1)
Catalyst : A Social Justice Forum     Open Access   (Followers: 10)
Catholic Social Science Review     Open Access   (Followers: 5)
CBU International Conference Proceedings     Open Access   (Followers: 3)
Cemoti, Cahiers d'études sur la méditerranée orientale et le monde turco-iranien     Open Access   (Followers: 4)
Challenges     Open Access   (Followers: 3)
Chandrakasem Rajabhat University Journal of Graduate School     Open Access  
Changing Societies & Personalities     Open Access  
China Journal of Social Work     Hybrid Journal   (Followers: 2)
Chinese Journal of Social Science and Management     Open Access  
Chinese Studies     Open Access   (Followers: 9)
Cidadania em Ação : Revista de Extensão e Cultura: Notícias     Open Access  
Ciencia e Interculturalidad     Open Access   (Followers: 3)
Ciência ET Praxis     Open Access  
Ciencia Sociales y Económicas     Open Access  
Ciencia y Sociedad     Open Access   (Followers: 3)
Ciencia, Cultura y Sociedad     Open Access   (Followers: 1)
Ciencia, Técnica y Mainstreaming Social     Open Access  
Ciencias Holguin     Open Access   (Followers: 3)
Ciências Sociais Unisinos     Open Access   (Followers: 3)
Ciencias Sociales y Educación     Open Access   (Followers: 5)
Ciencias Sociales y Humanidades     Open Access   (Followers: 4)
Ciencias Sociales y Religión/Ciências Sociais e Religião     Open Access  
CienciaUAT     Open Access   (Followers: 1)
Científic@ : Multidisciplinary Journal     Open Access  
Citizen Science : Theory and Practice     Open Access   (Followers: 1)
Citizenship Teaching & Learning     Hybrid Journal   (Followers: 9)
Ciudad Paz-ando     Open Access   (Followers: 1)
Civilizar Ciencias Sociales y Humanas     Open Access   (Followers: 3)
Civitas - Revista de Ciências Sociais     Open Access   (Followers: 3)
Claroscuro     Open Access   (Followers: 1)
CLIO América     Open Access   (Followers: 2)
CMU Journal of Law and Social Sciences     Open Access   (Followers: 2)
Cogent Social Sciences     Open Access   (Followers: 4)
Cognitive and Behavioral Practice     Hybrid Journal   (Followers: 13)
Colección Académica de Ciencias Sociales     Open Access  
Colonial Academic Alliance Undergraduate Research Journal     Open Access   (Followers: 4)
Communication, Politics & Culture     Open Access   (Followers: 14)
Communities, Children and Families Australia     Full-text available via subscription   (Followers: 4)
Community Empowerment     Open Access  
Compendium     Open Access   (Followers: 2)
Comprehensive Results in Social Psychology     Hybrid Journal   (Followers: 1)
Comprehensive Therapy     Hybrid Journal   (Followers: 3)
Comuni@cción     Open Access   (Followers: 1)
Comunitania : Revista Internacional de Trabajo Social y Ciencias Sociales     Open Access   (Followers: 1)
ConCiencia     Open Access  
Confluenze Rivista di Studi Iberoamericani     Open Access  
Connections     Open Access  
Contemporary Journal of African Studies     Full-text available via subscription   (Followers: 4)
Contemporary Social Science     Hybrid Journal   (Followers: 14)
CONTRA : RELATOS desde el Sur     Open Access  

        1 2 3 4 5 6     

Similar Journals
Journal Cover
Big Data & Society
Number of Followers: 52  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2053-9517
Published by Sage Publications Homepage  [1093 journals]
  • A post-truth pandemic'

    • Authors: Taylor Shelton
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      As the coronavirus pandemic continues apace in the United States, the dizzying amount of data being generated, analyzed and consumed about the virus has led to calls to proclaim this the first ‘data-driven pandemic’. But at the same time, it seems that this plethora of data has not meant a better grasp on the reality of the pandemic and its effects. Even as we have the potential to digitally track and trace nearly every single individual who has contracted the virus, we have no idea exactly how many people have had the virus, been hospitalized, or died because of it, largely due to a confluence of factors, particularly active obfuscation and mismanagement by public authorities and misinformation spread through social media and right-wing media channels. But beyond these dynamics, there also lies the less nefarious ways that the everyday, subjective practices of data collection, analysis and visualization have the potential to themselves (re)produce these very same dynamics where data is at once valorized and ignored, preeminent and completely useless. That is, the pandemic has revealed only the general inadequacy of our data infrastructures and assemblages to solving pressing social issues, but also the more general shift towards a ‘post-truth’ disposition in contemporary social life. But, as this paper argues, it would be a mistake to see the centrality of data as being somehow the opposite from the larger post-truth apparatus, as the two are instead fundamentally intertwined and co-produced.
      Citation: Big Data & Society
      PubDate: 2020-10-21T09:24:36Z
      DOI: 10.1177/2053951720965612
      Issue No: Vol. 7, No. 2 (2020)
       
  • Techno-solutionism and the standard human in the making of the COVID-19
           pandemic

    • Authors: Stefania Milan
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Quantification is particularly seductive in times of global uncertainty. Not surprisingly, numbers, indicators, categorizations, and comparisons are central to governmental and popular response to the COVID-19 pandemic. This essay draws insights from critical data studies, sociology of quantification and decolonial thinking, with occasional excursion into the biomedical domain, to investigate the role and social consequences of counting broadly defined as a way of knowing about the virus. It takes a critical look at two domains of human activity that play a central role in the fight against the virus outbreak, namely medical sciences and technological innovation. It analyzes their efforts to craft solutions for their user base and explores the unwanted social costs of these operations. The essay argues that the over-reliance of biomedical research on “whiteness” for lab testing and the techno-solutionism of the consumer infrastructure devised to curb the social costs of the pandemic are rooted in a distorted idea of a “standard human” based on a partial and exclusive vision of society and its components, which tends to overlook alterity and inequality. It contends that to design our way out of the pandemic, we ought to make space for distinct ways of being and knowing, acknowledging plurality and thinking in terms of social relations, alterity, and interdependence.
      Citation: Big Data & Society
      PubDate: 2020-10-21T02:36:14Z
      DOI: 10.1177/2053951720966781
      Issue No: Vol. 7, No. 2 (2020)
       
  • Revisiting the Black Box Society by rethinking the political economy of
           big data

    • Authors: Benedetta Brevini, Frank Pasquale
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The Black Box Society was one of first scholarly accounts to propose a social theory of the use of data in constructing personal reputations, new media audiences, and financial power, by illuminating recurrent patterns of power and exploitation in the digital economy. While many corporations have a direct window into our lives through constant, ubiquitous data collection, our knowledge of their inner workings is often partial and incomplete. Closely guarded by private companies and inaccessible to most researchers or the broader public, too much algorithmic decision-making remains a black box to this day. Much has happened since 2015 that vindicates and challenges the book’s main themes. To answer many of the concerns raised in the volume in light of the most recent developments, we have brought together leading thinkers who have explored the interplay of politics, economics, and culture in domains ordered algorithmically by managers, bureaucrats, and technology workers. While the contributions are diverse, a unifying theme animates them. Each offers a sophisticated critique of the interplay between state and market forces in building or eroding the many layers of our common lives, as well as the privatization of spheres of reputation, search, and finance. Unsatisfied with narrow methodologies of economics or political science, they advance politico-economic analysis. They therefore succeed in unveiling the foundational role that the turn to big data has in organizing economic and social relations.
      Citation: Big Data & Society
      PubDate: 2020-10-20T05:17:15Z
      DOI: 10.1177/2053951720935146
      Issue No: Vol. 7, No. 2 (2020)
       
  • The “black box” at work

    • Authors: Ifeoma Ajunwa
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further still to algorithmic processes of evaluations which will make authoritative claims as to the workers’ productivity. Furthermore, in and out of the workplace, the worker is governed by an invisible data-created leash deploying wearable technology to collect intimate worker data. At all stages, the worker is confronted with a lack of transparency, accountability, or explanation as to the inner workings or even the logic of the “black box” at work. This data revolution of the workplace is alarming for several reasons: (1) the “black box at work” not only serves to conceal disparities in hiring, but could also allow for a level of “data-laundering” that beggars any notion of equal opportunity in employment and (2) there exists, the danger of a “mission creep” attitude to data collection that allows for pervasive surveillance, contributing to the erosion of both the personhood and autonomy of workers. Thus, the “black box at work” not only enables worker domination in the workplace, it deprives the worker of Rawlsian justice.
      Citation: Big Data & Society
      PubDate: 2020-10-20T04:17:59Z
      DOI: 10.1177/2053951720938093
      Issue No: Vol. 7, No. 2 (2020)
       
  • Data/infrastructure in the smart city: Understanding the infrastructural
           power of Citymapper app through technicity of data

    • Authors: Güneş Tavmen
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Over the last few years, smart cities have been a focus of scholarly attention. Most of these critical studies concentrated on the multinational corporations’ discourses and their implications on urban policies. Besides these factors, however, the data-driven city develops within a complex web of entanglements whereby data-driven technologies modulate the urban infrastructure in a multitude of ways contingent upon the social, political, material and technical aspects. As such, this article attends to the infrastructural implications of a smart city product, Citymapper, which is a transport app built on open data available as part of London’s smart city planning. In order to establish the relationship between data and infrastructure, I use Gilbert Simondon’s notions of ‘transduction’, ‘individuation’ and ‘technicity’ to explore this relationship in a processual and relational way. In constructing this relationship as co-generative, whereby infrastructure and data transindividuate, I subsequently posit the term data/infrastructure. Against this theoretical background, I study the ways in which Citymapper individuates and thereby gains infrastructural power through technicity of data by studying the ways in which the users’ contribute to data generation that feed back into the app. Specifically, by following the transformation of the app from initially mediating the bus timetable to transducing users into environmental sensing nodes through which the app collects behavioural data, I foreground the epistemological, infrastructural and social consequences of Citymapper’s infrastructural power for the data-driven city.
      Citation: Big Data & Society
      PubDate: 2020-10-19T07:43:21Z
      DOI: 10.1177/2053951720965618
      Issue No: Vol. 7, No. 2 (2020)
       
  • Good organizational reasons for better medical records: The data work of
           clinical documentation integrity specialists

    • Authors: Kathleen H Pine, Claus Bossen
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Healthcare organizations and workers are under pressure to produce increasingly complete and accurate data for multiple data-intensive endeavors. However, little research has examined the emerging occupations arising to carry out the data work necessary to produce “improved” data sets, or the specific work activities of these emerging data occupations. We describe the work of Clinical Documentation Integrity Specialists (CDIS), an emerging occupation that focuses on improving clinical documentation to produce more detailed and accurate administrative datasets crucial for evolving data-intensive forms of healthcare accountability, management, and research. Using ethnographic methods, we describe the core of CDIS’ work as a translation practice in which the language, interests, and concerns of clinicians and clinical documentation are translated via real-time “nudging” and ongoing education of clinicians into the language, interests, and concerns of medical coders, structured administrative datasets, and the various stakeholders of these datasets. Further, we show how the institutional context of CDIS’ work shapes the occupational virtues that guide CDIS’ translation practice, including financial reimbursement, quality measures, clinical accuracy, and protecting clinician’s time. Despite the existence of these multiple virtues, financial reimbursement is the most prominent virtue guiding CDIS’ limited attention. Thus, overall clinical documentation is “improved” in specific, partial ways. This research provides one of the first studies of the emergent data work occupations arising in the wake of digitization and big data opportunities, and shows how local data settings shape large scale data in specific ways and thus may influence outcomes of analyses based on such data.
      Citation: Big Data & Society
      PubDate: 2020-10-19T07:38:40Z
      DOI: 10.1177/2053951720965616
      Issue No: Vol. 7, No. 2 (2020)
       
  • The value of sharing: Branding and behaviour in a life and health
           insurance company

    • Authors: Hugo Jeanningros, Liz McFall
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      As Big Data, the Internet of Things and insurance collide, so too, do the best and the worst of our futures. Insurance is summoned as an example of the interference in our private lives that is already underway everywhere. In this paper, we pause to reflect on this argument. Can changes in the way insurance measures the value of behaviour really serve as an example of the individual and social harms of datafication' How do we know' Insurance is a mathematical relationship staged between individuals and groups, between risk and uncertainty, between distribution and assessment, between the value of sharing and the sharing of value. We use the case study of Discovery International, owner of Vitality, the market leading brand in behavioural insurance to consider how behaviour is being branded and how the brand behaves.
      Citation: Big Data & Society
      PubDate: 2020-09-11T06:12:07Z
      DOI: 10.1177/2053951720950350
      Issue No: Vol. 7, No. 2 (2020)
       
  • Mass personalization: Predictive marketing algorithms and the reshaping of
           consumer knowledge

    • Authors: Baptiste Kotras
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      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.
      Citation: Big Data & Society
      PubDate: 2020-09-10T10:03:05Z
      DOI: 10.1177/2053951720951581
      Issue No: Vol. 7, No. 2 (2020)
       
  • Contested technology: Social scientific perspectives of behaviour-based
           insurance

    • Authors: Maiju Tanninen
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In this review, I analyse how ‘behaviour-based personalisation’ in insurance – that is, insurers’ increased interest in tracking and manipulating insureds’ behaviour with, for instance, wearable devices – has been approached in recent social scientific literature. In the review, I focus on two streams of literature, critical data studies and the sociology of insurance, discussing the new (i.e. health and life) insurance schemes that utilise sensor-generated and digital data. The aim of this review is to compare these two approaches and to analyse what kinds of understandings, methodologies and theoretical perspectives they apply to so-called ‘behaviour-based insurance’. The critical data studies literature emphasises the exploitative aspects of these new technologies and mobilises behaviour-based insurance to exemplify the negative outcomes of digital health. Scholars from the field of the sociology of insurance empirically analyse the practices of behavioural-based personalisation and study how regulating and ‘doing’ insurance affect attempts to personalise it. I highlight the importance of approaching insurance as a specific financial technology and argue that more research is needed to understand the practices of developing behaviour-based insurance schemes and the insureds’ experiences.
      Citation: Big Data & Society
      PubDate: 2020-09-07T07:44:21Z
      DOI: 10.1177/2053951720942536
      Issue No: Vol. 7, No. 2 (2020)
       
  • Emerging models of data governance in the age of datafication

    • Authors: Marina Micheli, Marisa Ponti, Max Craglia, Anna Berti Suman
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.
      Citation: Big Data & Society
      PubDate: 2020-09-01T08:37:44Z
      DOI: 10.1177/2053951720948087
      Issue No: Vol. 7, No. 2 (2020)
       
  • Epistemic clashes in network science: Mapping the tensions between
           idiographic and nomothetic subcultures

    • Authors: Mathieu Jacomy
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      This article maps a controversy in network science over the last 15 years, dividing the field about the epistemic status of a central notion, scale-freeness. The article accounts for the two main disputes, in 2005 and in 2018, as they unfolded in academic publications and on social media. This article analyzes the conflict, and the reasons why it reignited in 2018, to the surprise of many. It is argued that (1) the concept of complex networks is shared by the distinct subcultures of theorists and experimentalists; and that (2) these subcultures have incompatible approaches to knowledge: nomothetic (scale-freeness is the sign of a universal law) and idiographic (scale-freeness is an empirical characterization). Following Galison, this article contends that network science is a trading zone where theorists and experimentalists can trade knowledge across the epistemic divide.
      Citation: Big Data & Society
      PubDate: 2020-08-31T05:59:42Z
      DOI: 10.1177/2053951720949577
      Issue No: Vol. 7, No. 2 (2020)
       
  • COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

    • Authors: Age Poom, Olle Järv, Matthew Zook, Tuuli Toivonen
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.
      Citation: Big Data & Society
      PubDate: 2020-08-27T01:43:22Z
      DOI: 10.1177/2053951720952088
      Issue No: Vol. 7, No. 2 (2020)
       
  • Cambridge Analytica’s black box

    • Authors: Margaret Hu
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The Cambridge Analytica–Facebook scandal led to widespread concern over the methods deployed by Cambridge Analytica to target voters through psychographic profiling algorithms, built upon Facebook user data. The scandal ultimately led to a record-breaking $5 billion penalty imposed upon Facebook by the Federal Trade Commission (FTC) in July 2019. The FTC action, however, has been criticized as failing to adequately address the privacy and other harms emanating from Facebook’s release of approximately 87 million Facebook users’ data, which was exploited without user authorization. This Essay summarizes the FTC’s response to the Cambridge Analytica–Facebook scandal. It concludes that the scandal focuses attention on the need to explore the potential for embedding due process-type inquiries and protections within the enforcement actions by regulatory agencies such as the FTC. These protections are increasingly important in addressing the problem of “black boxing the voter” that is now presented by data- and algorithmic-driven companies such as Cambridge Analytica and Facebook.
      Citation: Big Data & Society
      PubDate: 2020-08-25T06:12:16Z
      DOI: 10.1177/2053951720938091
      Issue No: Vol. 7, No. 2 (2020)
       
  • Content moderation, AI, and the question of scale

    • Authors: Tarleton Gillespie
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      AI seems like the perfect response to the growing challenges of content moderation on social media platforms: the immense scale of the data, the relentlessness of the violations, and the need for human judgments without wanting humans to have to make them. The push toward automated content moderation is often justified as a necessary response to the scale: the enormity of social media platforms like Facebook and YouTube stands as the reason why AI approaches are desirable, even inevitable. But even if we could effectively automate content moderation, it is not clear that we should.
      Citation: Big Data & Society
      PubDate: 2020-08-21T09:02:40Z
      DOI: 10.1177/2053951720943234
      Issue No: Vol. 7, No. 2 (2020)
       
  • Designing for human rights in AI

    • Authors: Evgeni Aizenberg, Jeroen van den Hoven
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence (AI) systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically and socially aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.
      Citation: Big Data & Society
      PubDate: 2020-08-19T06:19:50Z
      DOI: 10.1177/2053951720949566
      Issue No: Vol. 7, No. 2 (2020)
       
  • Disruption and dislocation in post-COVID futures for digital health

    • Authors: Richard Milne, Alessia Costa
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In this piece we explore the COVID pandemic as an opportunity for the articulation and realization of digital health futures. Our discussion draws on an engagement with emergent discourse around COVID-19 and ongoing work on imaginaries of future care associated with digital tools for the detection of cognitive decline and the risk of dementia. We describe how the post-COVID futures of digital health are narrated in terms of the timing and speed with which they are being brought into being, as market actors attempt to establish the scale and durability of the COVID transformation. However, we also point to the particularly spatial changes to medical practice they envisage. In a time of distancing and isolation, the ability to operate effectively at a distance has become integral to the future of medical assessment, diagnosis and care. However, spatialized promises of digital health and the ability to act remotely are unevenly spread – some organizations and entities inevitably have greater reach.
      Citation: Big Data & Society
      PubDate: 2020-08-18T04:22:05Z
      DOI: 10.1177/2053951720949567
      Issue No: Vol. 7, No. 2 (2020)
       
  • Corrigendum to “Data as performance – Showcasing cities
           through open data maps”

    • Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.

      Citation: Big Data & Society
      PubDate: 2020-08-17T06:08:08Z
      DOI: 10.1177/2053951720951561
      Issue No: Vol. 7, No. 2 (2020)
       
  • Intentionality and design in the data sonification of social issues

    • Authors: Sara Lenzi, Paolo Ciuccarelli
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Data sonification is a practice for conducting scientific analysis through the use of sound to represent data. It is now transitioning to a practice for communicating and reaching wider publics by expanding the range of languages and senses for understanding complexity in data-intensive societies. Communicating to wider publics, though, requires that authors intentionally shape sonification in ways that consider the goals and contexts in which publics relate. It requires a specific set of knowledge and skills that design as a discipline could provide. In this article, we interpret five recent sonification projects and locate them on a scale of intentionality in how authors communicate socially relevant issues to publics.
      Citation: Big Data & Society
      PubDate: 2020-08-17T05:29:35Z
      DOI: 10.1177/2053951720944603
      Issue No: Vol. 7, No. 2 (2020)
       
  • The politics of algorithmic governance in the black box city

    • Authors: Gavin JD Smith
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Everyday surveillance work is increasingly performed by non-human algorithms. These entities can be conceptualised as machinic flâneurs that engage in distanciated flânerie: subjecting urban flows to a dispassionate, calculative and expansive gaze. This paper provides some theoretical reflections on the nascent forms of algorithmic practice materialising in two Australian cities, and some of their implications for urban relations and social justice. It looks at the idealisation – and operational black boxing – of automated watching programs, before considering their impacts on notions such as ‘the right to the city’ and ‘the right to the face’. It will argue that the turn to facial recognition software for the purposes of automating urban governance reconstitutes the meanings and phenomenology of the face. In particular, the fleshly and communicative physicality of the face is reduced to a measurable object that can be identified by a virtualised referent and then consequently tracked. Moreover, the asymmetrical and faceless nature of these machinic programs of recognition unsettles conventional notions of civil inattention and bodily sovereignty, and the prioritisation given to pattern recognition renders them amenable to ideas/ideals from phrenology and physiognomy. In this way, algorithmic governance may generate not only forms of facial vulnerability and estrangement, but also facial artifice, where individuals come to develop tacit and artful ways of de-facing and re-facing in order to subvert the processes of recognition which leverage these modes of biopower. Thus, the datafication of urban governance gives rise to a dynamic biopolitics of the face.
      Citation: Big Data & Society
      PubDate: 2020-08-14T11:02:20Z
      DOI: 10.1177/2053951720933989
      Issue No: Vol. 7, No. 2 (2020)
       
  • From pool to profile: Social consequences of algorithmic prediction in
           insurance

    • Authors: Alberto Cevolini, Elena Esposito
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage' In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour.
      Citation: Big Data & Society
      PubDate: 2020-07-30T07:20:55Z
      DOI: 10.1177/2053951720939228
      Issue No: Vol. 7, No. 2 (2020)
       
  • Seven intersectional feminist principles for equitable and actionable
           COVID-19 data

    • Authors: Catherine D'Ignazio, Lauren F. Klein
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism (D'Ignazio and Klein, 2020), offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning the work of change.
      Citation: Big Data & Society
      PubDate: 2020-07-30T05:56:41Z
      DOI: 10.1177/2053951720942544
      Issue No: Vol. 7, No. 2 (2020)
       
  • Big Data: From modern fears to enlightened and vigilant embrace of new
           beginnings

    • Authors: Nicole Dewandre
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In The Black Box Society, Frank Pasquale develops a critique of asymmetrical power: corporations’ secrecy is highly valued by legal orders, but persons’ privacy is continually invaded by these corporations. This response proceeds in three stages. I first highlight important contributions of The Black Box Society to our understanding of political and legal relationships between persons and corporations. I then critique a key metaphor in the book (the one-way mirror, Pasquale’s image of asymmetrical surveillance), and the role of transparency and ‘watchdogging’ in its primary policy prescriptions. I then propose ‘relational selfhood’ as an important new way of theorizing interdependence in an era of artificial intelligence and Big Data, and promoting optimal policies in these spheres.
      Citation: Big Data & Society
      PubDate: 2020-07-29T06:07:56Z
      DOI: 10.1177/2053951720936708
      Issue No: Vol. 7, No. 2 (2020)
       
  • Contesting algorithms: Restoring the public interest in content filtering
           by artificial intelligence

    • Authors: Niva Elkin-Koren
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. Artificial intelligence filters carry censorial power, which could bypass traditional checks and balances secured by law. Their opaque and dynamic nature creates barriers to oversight, and conceals critical value choices and tradeoffs. Currently, we lack adequate tools to hold them accountable. This paper seeks to address this gap by introducing an adversarial procedure— – Contesting Algorithms. It proposes to deliberately introduce friction into the dominant removal systems governed by artificial intelligence. Algorithmic content moderation often seeks to optimize a single goal, such as removing copyright-infringing materials or blocking hate speech, while other values in the public interest, such as fair use or free speech, are often neglected. Contesting algorithms introduce an adversarial design which reflects conflicting values, and thereby may offer a check on dominant removal systems. Facilitating an adversarial intervention may promote democratic principles by keeping society in the loop. An adversarial public artificial intelligence system could enhance dynamic transparency, facilitate an alternative public articulation of social values using machine learning systems, and restore societal power to deliberate and determine social tradeoffs.
      Citation: Big Data & Society
      PubDate: 2020-07-29T06:00:53Z
      DOI: 10.1177/2053951720932296
      Issue No: Vol. 7, No. 2 (2020)
       
  • “Smittestopp”: If you want your freedom back, download now

    • Authors: Kristin B Sandvik
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The intervention attempts to engage critically with the Smittestopp app as a specifically Norwegian technofix. Culturally and politically, much of the Covid-19 response and the success of social distancing rules have been organized around the widespread trust in the government and public health authorities, and a focus on the citizens’ duty to contribute to the dugnaðr. The intervention argues that Smittestopp has been co-created by the mobilization of trust and dugnaðr, resulting in the launch of an incomplete and poorly defined data-hoarding product with significant vulnerabilities.
      Citation: Big Data & Society
      PubDate: 2020-07-28T08:07:22Z
      DOI: 10.1177/2053951720939985
      Issue No: Vol. 7, No. 2 (2020)
       
  • “No disease for the others”: How COVID-19 data can enact new
           and old alterities

    • Authors: Annalisa Pelizza
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The COVID-19 pandemic invites a question about how long-standing narratives of alterity and current narratives of disease are entwined and re-enacted in the diagnosis of COVID-19. In this commentary, we discuss two related phenomena that, we argue, should be taken into account in answering this question. First, we address the diffusion of pseudoscientific accounts of minorities’ immunity to COVID-19. While apparently praising minorities’ biological resistance, such accounts rhetorically introduce a distinction between “Us” and “Them,” and in so doing produce new and re-enact old narratives of alterity. Second, these unsubstantiated narratives thrive on fake news and scarcity of data. The second part of this commentary thus surveys the methods through which the COVID-19 test is administered in various countries. We argue that techniques used for data collection have a major role in producing COVID-19 data that render contagion rates among migrants and other minorities invisible. In the conclusion, we provide two recommendations about how COVID-19 data can instead potentially work towards inclusion.
      Citation: Big Data & Society
      PubDate: 2020-07-28T05:40:52Z
      DOI: 10.1177/2053951720942542
      Issue No: Vol. 7, No. 2 (2020)
       
  • Covid-19 and the accelerating smart home

    • Authors: Sophia Maalsen, Robyn Dowling
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Home, digital technologies and data are intersecting in new ways as responses to the COVID-19 pandemic emerge. We consider the data practices associated with COVID-19 responses and their implications for housing and home through two overarching themes: the notion of home as a private space, and digital technology and surveillance in the home. We show that although home has never been private, the rapid adoption and acceptance of technologies in the home for quarantine, work and study, enabled by the pandemic, is rescripting privacy. The acceleration of technology adoption and surveillance in the home has implications for privacy and potential discrimination, and should be approached with a critical lens.
      Citation: Big Data & Society
      PubDate: 2020-07-28T05:31:16Z
      DOI: 10.1177/2053951720938073
      Issue No: Vol. 7, No. 2 (2020)
       
  • Learning from lines: Critical COVID data visualizations and the quarantine
           quotidian

    • Authors: Emily Bowe, Erin Simmons, Shannon Mattern
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. Second, they demonstrate how the pandemic’s spatial logics inscribe themselves in our immediate material landscapes. And third, they remind us of our capacity to personalize and participate in the creation of meaningful COVID visualizations—many of which represent other scales and dimensions of the pandemic, especially the quarantine quotidian. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care.
      Citation: Big Data & Society
      PubDate: 2020-07-27T05:34:41Z
      DOI: 10.1177/2053951720939236
      Issue No: Vol. 7, No. 2 (2020)
       
  • Making data science systems work

    • Authors: Samir Passi, Phoebe Sengers
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      How are data science systems made to work' It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. These negotiations laid the foundation for how, why, and to what extent the system ultimately worked. We describe three main findings. First, how already-existing technologies are essential reference points to determine how and whether systems work. Second, how the situated resolution of development challenges continually reshapes the understanding of how and whether systems work. Third, how business goals, and especially their negotiated balance with data science imperatives, affect a system’s working. We conclude with takeaways for critical data studies, orienting researchers to focus on the organizational and cultural aspects of data science, the third-party platforms underlying data science systems, and ways to engage with practitioners’ imagination of how systems can and should work.
      Citation: Big Data & Society
      PubDate: 2020-07-27T05:14:34Z
      DOI: 10.1177/2053951720939605
      Issue No: Vol. 7, No. 2 (2020)
       
  • AI ethics should not remain toothless! A call to bring back the teeth of
           ethics

    • Authors: Anaïs Rességuier, Rowena Rodrigues
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics can achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now.
      Citation: Big Data & Society
      PubDate: 2020-07-22T11:40:32Z
      DOI: 10.1177/2053951720942541
      Issue No: Vol. 7, No. 2 (2020)
       
  • The price of certainty: How the politics of pandemic data demand an ethics
           of care

    • Authors: Linnet Taylor
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The Covid-19 pandemic broke on a world whose grip on epistemic trust was already in disarray. The first months of the pandemic saw many governments publicly performing reliance on epidemiological and modelling expertise in order to signal that data would be the basis for justifying whatever population-level measures of control were judged necessary. But comprehensive data has not become available, and instead scientists, policymakers and the public find themselves in a situation where policy inputs determine the data available and vice versa. This essay asks how we can live with what Amoore has termed ‘post-Cartesian doubt’ in situations of existential risk, and what kind of approach to science and data can answer the moral and human demands of a situation such as the Covid-19 pandemic. I suggest that science and policy could be able to control the pandemic better by addressing the sources of uncertainty and missing data not as gaps in the information landscape, but as individuals who are likely to be members of less-visible and less powerful groups including low-wage workers, the elderly, migrants, prisoners and others. This would shift both data use and policy toward an ethics of care, an embodied approach which asks what people need and how they behave in relation to each other, rather than how to manage population-level behaviour. This approach, I argue, is more appropriate for pandemic response than a utilitarian calculation of how many people each country should expect to lose as a result of the disease.
      Citation: Big Data & Society
      PubDate: 2020-07-22T11:40:16Z
      DOI: 10.1177/2053951720942539
      Issue No: Vol. 7, No. 2 (2020)
       
  • Going viral: How a single tweet spawned a COVID-19 conspiracy theory on
           Twitter

    • Authors: Anatoliy Gruzd, Philip Mai
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospitals to help “prove” that the COVID-19 pandemic is an elaborate hoax. Using techniques from Social Network Analysis, this case study examines how this conspiracy theory propagated on Twitter and whether the hashtag virality was aided by the use of automation or coordination among Twitter users. We found that while much of the content came from users with limited reach, the oxygen that fueled this conspiracy in its early days came from a handful of prominent conservative politicians and far right political activists on Twitter. These power users used this hashtag to build awareness about the campaign and to encourage their followers to break quarantine and film what is happening at their local hospitals. After the initial boost by a few prominent accounts, the campaign was mostly sustained by pro-Trump accounts, followed by a secondary wave of propagation outside the U.S. The rise of the #FilmYourHospital conspiracy from a single tweet demonstrates the ongoing challenge of addressing false, viral information during the COVID-19 pandemic. While the spread of misinformation can be potentially mitigated by fact-checking and directing people to credible sources of information from public health agencies, false and misleading claims that are driven by politics and supported by strong convictions and not science are much harder to root out.
      Citation: Big Data & Society
      PubDate: 2020-07-21T04:41:40Z
      DOI: 10.1177/2053951720938405
      Issue No: Vol. 7, No. 2 (2020)
       
  • A proxy for privacy uncovering the surveillance ecology of mobile apps

    • Authors: Signe Sophus Lai, Sofie Flensburg
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      The article develops a methodological and empirical approach for gauging the ways Big Data can be collected and distributed through mobile apps. This approach focuses on the infrastructural components that condition the disclosure of smartphone users’ data – namely the permissions that apps request and the third-party corporations they work with. We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone users and investigates the consequences of which and how many mobile apps users download on their phones; the second compares different types of apps in order to study the app ecology and the relationships between app and third-party service providers; and the third focuses on a particular app category and discusses the functional as well as the commercial incentives for permissions and third-party collaborations. Thereby, the article advances an interdisciplinary dialogue between critical data studies, political economy and app studies, and pushes an empirical and critical perspective on mobile communication, app ecologies and data economies.
      Citation: Big Data & Society
      PubDate: 2020-07-16T04:55:29Z
      DOI: 10.1177/2053951720942543
      Issue No: Vol. 7, No. 2 (2020)
       
  • Black boxes, not green: Mythologizing artificial intelligence and omitting
           the environment

    • Authors: Benedetta Brevini
      Abstract: Big Data & Society, Volume 7, Issue 2, July-December 2020.
      We are repeatedly told that AI will help us to solve some of the world's biggest challenges, from treating chronic diseases and reducing fatality rates in traffic accidents to fighting climate change and anticipating cybersecurity threats. However, the article contends that public discourse on AI systematically avoids considering AI’s environmental costs.Artificial Intelligence- Brevini argues- runs on technology, machines, and infrastructures that deplete scarce resources in their production, consumption, and disposal, thus increasing the amounts of energy in their use, and exacerbate problems of waste and pollution. It also relies on data centers, that demands impressive amounts of energy to compute, analyse, categorize. If we want to stand a chance at tackling the Climate Emergency, then we have to stop avoiding addressing the environmental problems generated by AI.
      Citation: Big Data & Society
      PubDate: 2020-07-06T04:58:48Z
      DOI: 10.1177/2053951720935141
      Issue No: Vol. 7, No. 2 (2020)
       
 
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