Tuesday 18 June 2024

BD&S Journal will be on break from Aug 4th to Sept 4th, 2024

 The editorial team of the journal Big Data & Society will be on break from August 4th to September 4th 2024.  Please accept any delays in processing and reviewing your submission, and in related correspondence during that time. Thank you!

Wednesday 29 May 2024

Guest Blog: Jascha Bareis on Trust and AI

by Jascha Bareis

Bareis, J. (2024). The trustification of AI. Disclosing the bridging pillars that tie trust and AI together. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241249430

Does it make sense to tie trust with AI?

Everywhere we look: companies, politics, and research: so many people are focusing on AI. Being approached as the core technological institution of our times, notions of trust are repeatedly mobilized. Especially policy makers seem to feel urged to highlight the need of trustworthiness in relation to AI. Take the current European AI act that claims the EU to be a “leader in the uptake of trustworthy AI” (AIA, Article 1a), or the US 2023 US executive order on “Safe, Secure and Trustworthy AI”.

I simply asked myself: Despite all this attention, is it at all legitimate to marry a technology with an intersubjective phenomenon that used to be reserved between humans only? I can trust my neighbor next door taking care of my cat, but can I trust the TESLA Smart Cat Toilet automating cleaning cycle to take care of its poo-poo, too (indeed, the process of gadget’ification and smartification does not spare cat toilets)?

Does it make sense at all to talk about trust in the latter case or are we just dealing with a conceptual misfit? Doing more research, I realized that the way trust is handled in both the policy and academic AI debate is very sloppy, staying undertheorized and just somehow taken for granted.

I noticed that users approach trust and AI as something intersubjective, expecting great things from their new AI powered gadget and then being utterly disappointed if it fails to do so (because even if branded as “smart”, there is actually no AI in the TESLA Smart Cat Toilet). Users perceive AI as something being highly mediated by powerful actors, as when Elon Musk trusts that AI will be the cure to the world’s problems, many people seem to follow blindly (but do they trust AI then, or Elon?). And as something that can mobilize greater political dimensions and strong sentiments. As when my friend told me that she would certainly distrust AI because she distrusted the integrity of EU politicians who instead of regulating it, let Big Tech get “greedy and rich”.

Communication, mediation, sentiments, expectations, power, misconceptions – all of this seemed to have a say in the relationship between AI and trust. This created a messy picture with AI and trust being enmeshed in a social complex interplay with overlapping epistemic realms and actors.

As a consequence, I set out to problematize this relationship in this paper. I argue that trust is located in the social. And only if one acknowledges that AI is a very social phenomenon as well, this relationship makes sense at all. AI produces notions of trust and distrust because it is woven and negotiated in the everyday realities of users and society, with AI applications mediating human relationships, producing intimacies, social orders and knowledge authorities. I came up with the following analytical scheme.

I run through the scheme in the paper and describe its value and limitations, rendering trustworthy AI as a constant and complex dynamic between the actual technological developments and the social realities, political power struggles and futures that are associated with it.

Monday 13 May 2024

Call for Special Theme Proposals for Big Data & Society (Due August 15, 2024)

 Call for Special Theme Proposals for Big Data & Society

The SAGE open access journal Big Data & Society (BD&S) is soliciting proposals for a Special Theme to be published in 2025/26. BD&S is a peer-reviewed, interdisciplinary, scholarly journal that publishes interdisciplinary social science research about the emerging field of Big Data practices and how they are reconfiguring relations, expertise, methods, concepts and knowledge across academic, social, cultural,  political, and economic realms. BD&S moves beyond usual notions of Big Data to engage with an emerging field of practices that is not defined by but generative of (sometimes) novel data qualities such as extensiveness, granularity, automation, and complex analytics including data linking and mining. The journal attends to digital content generated through online and offline practices, including social media, search engines, Internet of Things devices, and digital infrastructures across closed and open networks, from commercial and government transactions to digital archives, open government and crowd-sourced data. Rather than settling on a definition of Big Data, the Journal makes this an area of interdisciplinary inquiry and debate explored through multiple disciplines and themes.

Special Themes can consist of a combination of Original Research Articles (6 maximum, up to 10,000 words each), Commentaries (4 maximum, 3,000 words each) and one Editorial Introduction (3,000 words). All Special Theme content will have the Article Processing Charges waived. All submissions will go through the Journal’s standard peer review process.

While open to submissions on any theme related to Big Data we particularly welcome proposals related to Big Data and the Global South / Global Majority; Indigenous data and data sovereignty; queer and trans data; and Big Data and racialization. You can find the full list of special themes published by BD&S at http://journals.sagepub.com/page/bds/collections/index 

Format of Special Theme Proposals

Researchers interested in proposing a Special Theme should submit an outline with the following information.


  • An overview of the proposed theme, including how it relates to existing research and the aims and scope of the Journal, and the ways it seeks to expand critical scholarly research on Big Data.

  • A list of titles, abstracts, authors and brief biographies. For each, the type of submission (ORA, Commentary) should also be indicated. If the proposal is the result of a workshop or conference that should also be indicated.

  • Short bios of the Guest Editors including affiliations and previous work in the field of Big Data studies. Links to homepages, Google Scholar profiles or CVs are welcome, although we don’t require CV submissions.

  • A proposed timing for submission to Manuscript Central. This should be in line with the timeline outlined below.


Information on the types of submissions published by the Journal and other guidelines is available at https://journals.sagepub.com/author-instructions/BDS  .


Timeline for Proposals

Submit proposals by August 15, 2024 via this online form https://forms.gle/VQZM4eSRZDAYgLvY9  

(Note: you must have a Google account in order to access this form). Do not send proposals via email as they will not be reviewed. The Editorial Team of BD&S will review proposals and make a decision by October 2024. Manuscripts would be submitted to the journal (via manuscript central) by or before February 2025. 

For further information or discuss potential themes please contact Dr. Matthew Zook at zook@uky.edu.


Monday 29 April 2024

Guest Blog: Data as environment, environment as data. One Health in collaborative data-intensive science

by Lucilla Barchetta (@lucilla_ba)

One Health considers health as interdependent across humans and non-humans. Its development is thus necessarily interdisciplinary, posing a number of challenges in ontoepistemic terms. Increasingly, technology and big data are seen as the solution to these challenges, allowing to aggregate heterogenous data. This article’s argument is that technology does not eliminate the need to agree on shared epistemological premises across disciplines, and this takes time and efforts of social coordination. 

Drawing insights from ethnographic field research, the article introduces the concept of ‘data as environment.’ Data isn’t just about bits and bytes; it’s about an interconnected structure that weaves together knowledge systems, individuals, processing tools, and bio-social interactions in the complex tapestry of data-intensive knowledge co-production. This environment becomes a contact structure, entangling not just data, but the very essence of collaborative efforts across biomedical, environmental, and social sciences. 

The concept of ‘data as environment’ is different from previous similar concepts such as “data infrastructures,” “data ecosystems,” “data communities,” “data journeys,” “data collaboratives” and “data cultures” because it does not posit something called “data” as distinguished from something called “environment”. Instead, we conceive data itself as an environment, a structure of contact that emerges in putting into relationship various elements, activated by use, maintenance and slippages. We, thus, address the mainstream criticism that data are self-contained bits, enclosed and autonomous objects, extracted and abstracted from the flux of becoming. 

The article makes a compelling point by unravelling political-ethical questions embedded in the emerging technoscientific worlds of the Anthropocene. It goes beyond the surface of data, prompting us to ponder the ethical implications and societal dimensions of the evolving landscape of data-intensive science. 

In essence, this article beckons us to embrace a holistic understanding of research – where data is not just information but an environment that shapes and is shaped by the collaborative efforts of diverse scientific subjectivities and entities. As we navigate the complexities of the Anthropocene, this exploration of 'data as environment' becomes a crucial lens through which we can better comprehend the dynamic interplay between data, knowledge, and the evolving landscape of interdisciplinary collaboration.

Tuesday 9 April 2024

Guest Blog: Role-Based Privacy Cynicism and Local Privacy Activism: How Data Stewards Navigate Privacy in Higher Education

by Mihaela Popescu, Lemi Baruh, and Samuel Sudhakar 

When was the last time you truly felt that adjusting your privacy settings on your most visited platform enhanced your safety? In today's digital age, especially in the United States, many users have come to accept that sacrificing privacy is an unavoidable consequence of engaging with digital technologies. This realization often breeds cynicism or apathy towards privacy, leading individuals to abandon efforts to safeguard their personal information.


This phenomenon, known by various names like privacy cynicism, privacy apathy or surveillance realism, encapsulates feelings of mistrust, powerlessness, and resignation that consumers commonly experience. While existing research focuses on data subjects' attitudes, our study presents a unique perspective – that of data workers who straddle the roles of both data subjects and data handlers in higher education settings. We aimed to explore the prevalence of privacy cynicism among these data workers and its potential impact on university data governance.


Projections indicate that the global market for big data analytics in education will exceed $50 billion by 2030. Within this landscape, university data professionals – including campus registrars, learning platform administrators, and information security officers – play a crucial role in safeguarding university data assets, albeit not always prioritizing the privacy of campus stakeholders. Our research, based on in-depth interviews with data professionals at California State University, unveiled significant findings:


1. Receptiveness to Datafication: Despite concerns about datafication trends, data professionals in higher education view its implementation as beneficial.

2. Tactics to Navigate Challenges: When faced with data misuse concerns, these professionals employ short-term "privacy activism" tactics to delay problematic uses.

3. Structural Changes vs. Short-Term Solutions: While effective in the short term, these tactics offer temporary fixes without fostering lasting structural changes.


Similar to consumer privacy cynicism, our interviews reflected a parallel sentiment among data professionals, particularly when organizational privacy definitions clashed with their personal beliefs. They grappled with powerlessness and disillusionment, exacerbated by the apathy shown by the very individuals they aim to protect.


A key insight from our study is the potential far-reaching consequences of this perception. A perceived lack of efficacy coupled with a perception that data subjects (namely, the students) don't care about privacy may lead to a spiral of resignation, reducing data professionals' motivation to advocate for enhanced privacy. This, in turn, limits data subjects' access to meaningful privacy options, further fueling their privacy apathy and cynicism.

Saturday 6 April 2024

Guest Blog: Situating Data Relations in the Datafied Home

by Gaia Amadori and Giovanna Mascheroni 

Situating data relations in the datafied home: A methodological approach. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241234268 

As data relations, namely relations and communicative practices that are mediated, sustained, and shaped by the digital technologies that extract data, are pervading practices and imaginaries of parenting and childhood, the challenge of empirically studying datafication becomes particularly prominent in this context.

To address the epistemological and methodological challenges in the study of datafication from an everyday life perspective, we propose to focus on mediatized relations as a proxy for data relations. More specifically, drawing upon a non-media-centric figurational approach, we argue for the value of combining mixed method constructivist grounded theory methodology with network methods so as to materialise the relationships through, about and around data that emerge in contemporary family life. We do this by focusing on 3 households from a group of 20 with at least 1 child aged 8 years or younger in Italy, who participated in a qualitative longitudinal study on the datafication of childhood and family life.

The study aims to delineate an innovative methodological approach to highlighting the situatedness of data practices and imaginaries and developing new research tools to enhance the phenomenological richness of data practices in the diverse digital–material contexts of family life. In particular, we show how different family figurations translate into different patterns of mediatized relations and, consequently, of data relations, depending on cultural coordinates, such as parenting and mediation styles, as well as data and digital media imaginaries. Furthermore, we suggest how network methods represent a suitable tool for materialising the mediatized relations structure, providing a set of metrics and visualizations that can foster researchers’ and participants’ reflexivity.

In addition, we believe this approach can be extended beyond the home to understand how data relations reconfigure different communicative figurations.

Wednesday 20 March 2024

Guest Blog: Mapping the landscape of cloud AI: Microsoft, Google, Amazon, and the ‘industrialisation’ of artificial intelligence

By Fernando van der Vlist (@fvandervlist) and Anne Helmond (@silvertje)

Van der Vlist, F. N., Helmond, A., & Ferrari, F. L. (2024). Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence. Big Data & Society, 11(1), 1–16.

Convergence of AI and Big Tech—The ongoing competition among tech giants in the ‘cloud AI wars’ is shaping a supposed transformative era. Industry leaders like Bill Gates and Sundar Pichai underscore the foundational role of AI. However, this transformation is chiefly propelled by a select few—Microsoft, Google (Alphabet), and Amazon. These giants hold sway over the cloud computing landscape, wielding profound influence.
Characterising the platformisation and ‘industrialisation’ of AI
Van der Vlist, Helmond, and Ferrari’s comprehensive landscape study, titled ‘Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence’, delves into the profound implications of the dominance wielded by these tech giants, introducing the term ‘industrialisation of AI’. This term captures the transition of AI systems from the realm of research and development to practical, ‘real-world’ applications across diverse industries. This transformation brings a new reliance on cloud infrastructure and substantial investments in computational resources, vital for the industrial-scale deployment of AI solutions. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform emerge as the linchpin cloud platforms underpinning this ongoing industrialisation process.
The ramifications of their influence became glaringly evident during an AWS outage on June 13, 2023. The disruptions faced by clients like the Associated Press, McDonald’s, and Reddit underscored their extensive reliance on AWS. Market estimates emphasise AWS’s dominance, serving as the backbone of the Internet, followed by Microsoft Azure and Google Cloud. The comprehensive suites of cloud products and services offered by these companies not only underscore their dominance but also significantly contribute to their revenues.
Moreover, discursively, the term ‘AI’ acts as a powerful magnet, attracting substantial investments and prompting startups to seek partnerships with major players. This includes (exclusive) cloud provider partnerships such as between Microsoft Azure and OpenAI (powering ChatGPT and DALL·E, amongst others). These tech giants actively position themselves as essential infrastructure providers, pouring billions into costly cloud computing. As AI enters its ‘industrial age’, understanding the intricacies of AI’s value chains becomes crucial for strategic, political, and economic reasons.
The dominance of major tech companies is intrinsically tied to their control over infrastructure. This dominance, fueled by access to vast troves of data, substantial computational resources, and a geopolitical edge, underscores their pivotal role in driving AI development and deployment. As succinctly put by Kak and Myers West, ‘There is no AI without Big Tech’.
A ‘technography’ of AI and Big Tech: Infrastructure, models, and applications
To capture this structural convergence between AI and Big Tech, Van der Vlist et al. conceptualise ‘Big AI’. This term characterises the intricate interdependence between AI and the infrastructure, resources, and investments of major tech conglomerates. This structural dependency is the cornerstone of the ongoing industrialisation of AI. Their empirical analysis further substantiates these critiques. While ‘Big AI’ isn’t the sole trajectory for the future of AI, the continuous provisioning of essential infrastructure services by Microsoft, Google (Alphabet), and Amazon positions them to reap the benefits of AI’s widespread expansion across industry sectors.
In their empirical exploration—characterised as a ‘technography of cloud AI’—, they engage with the material aspects of cloud AI to examine its structural and operational features. They uncover various forms of support and investment and scrutinise the cloud platform offerings from Microsoft, Google, and Amazon. This comprehensive approach provides unique insights into the current state and evolution of ‘Big AI’, offering a profound understanding of AI as both a product and service category, and an integral component of existing cloud computing arrangements. Furthermore, their study sheds light on the developmental and deployment aspects of the purported ‘AI revolution’, heralded by ChatGPT’s launch in late 2022, highlighting the substantial role played by Microsoft, Google, and Amazon in convening enterprises, organisations, and developers, fostering the creation, capture, and commercialisation of AI.


Cloud AI stacks: Structural interconnections among cloud platform products and services offered by Microsoft Azure, Google Cloud Platform, and Amazon Web Services. https://doi.org/10.17605/osf.io/unvc2

Ultimately, the study goes beyond characterising the current ‘platformisation’ of AI, where AI expands beyond consumer-facing applications like ChatGPT to become a platform service provided by Big Tech companies (i.e. an AI platform and infrastructure as a service). This encompasses extensive suites of tools, products, and services—from hardware AI infrastructure to machine learning and computer vision software—, along with ‘platform boundary resources’ for developers and businesses to build upon. The study comprehensively analyses and substantiates this transformation with empirical evidence. It highlights how Big AI represents a dual form of power: first, by owning and offering essential infrastructure and support, and second, by controlling marketplaces for the distribution and deployment of AI models and applications across diverse sectors and industries. Additionally, the study leverages the empirical analysis to conceptualise AI’s cloud infrastructure dependence and the ongoing ‘industrialisation’ of AI, providing important guidance for policymakers and regulators in governing AI.
The full research article is openly available in Big Data & Society at https://doi.org/10.1177/20539517241232630. The data that support the findings of this study are openly available in the Open Science Framework (OSF) at https://doi.org/10.17605/osf.io/unvc2.