Guest Blog: How authoritarian ideas about AI governance shape global debates

by Gregory Asmolov

Asmolov, G. (2026). The rise of AI sovereignty: Authoritarian technological imaginaries as a form of reflexive control. Big Data & Society, 13(2), 20539517261426

Much of the current debate about the contribution of artificial intelligence to the rise of digital authoritarianism focuses on its role in disinformation, propaganda, and surveillance. But authoritarian influence may also operate at a deeper level, by shaping how AI itself is governed. This question motivated my recent article in Big Data & Society.

The purpose of the article is to explore how authoritarian ideas travel across borders and influence global AI governance. Rather than focusing on the export of technologies and AI-enabled practices, it examines mechanisms of authoritarian diffusion through informational influence, showing how narratives and ideas shape the forms AI governance takes.

The analysis focuses on the rise of “AI sovereignty” as a central concept in global debates. Drawing on the notion of sociotechnical imaginaries and the concept of reflexive control from Soviet strategic thought, the article shows how political actors can influence others by shaping perceptions of technological futures.

Using Russia as a case study, the article analyses authoritarian imaginaries that inform approaches to AI governance. It identifies three dominant narratives: AI as a tool of global power and domination; “Western AI” as a cultural threat; and AI as a driver of state efficiency. It then examines how these narratives may extend beyond Russia, influencing global debates and contributing to the spread of sovereignty-centred approaches to AI governance.

The main argument is that these imaginaries influence the cognitive environment of global policymaking. As they circulate, they promote sovereignty-centred and security-driven approaches to AI governance, even beyond authoritarian contexts.

Recognising this diffusion mechanism highlights the need for greater sensitivity to the potential impact of authoritarian imaginaries on global models of AI governance. Developing such awareness is essential for mitigating the subtle spread of authoritarian logics through policy debates and regulatory frameworks. The article contributes to this effort by advancing a theoretical understanding of how diffusion operates through informational influence, and by showing specifically how it can shape the governance of AI across different political contexts.

Guest Blog: AI Failure Loops in devalued work: The confluence of overconfidence in AI and underconfidence in worker expertise

by Anna Kawakami

Kawakami, A., Taylor, J., Fox, S., Zhu, H., & Holstein, K. (2026). AI failure loops in devalued work: The confluence of overconfidence in AI and underconfidence in worker expertise. Big Data & Society, 13(1), 20539517261424164.

From headlines to prediction markets, AI’s impact on work is increasingly framed as a question of which jobs will disappear. Yet, in many workplaces, the more immediate issue is not displacement but failure. In historically devalued fields such as K-12 teaching, social work, and home health care, AI systems are often built on reductionist understandings of human work, leading to deployments that fall short in patterned ways. We describe these recurring breakdowns as AI Failure Loops: dynamics in which the devaluation of worker expertise shapes flawed AI systems—and those systems, in turn, further erode recognition of that expertise. 

We conduct a focused review of academic and grey literature on AI deployments across three domains of devalued, feminized labor 1 in the United States (social work, home health, K-12 teaching), grounded in our team’s direct experiences studying and designing AI alongside workers in these fields. Through three case studies—AI-based risk assessments in child welfare, AI for home healthcare, and AI tutoring systems in K-12 teaching—we illustrate how AI Failure Loops arise in practice. We identify a set of six interconnected failure modes in the design, development, and deployment of AI systems that underlie AI Failure Loops: Expertise Misunderstanding, Managerial Over Worker Needs, Design Exclusion, Inappropriate Evaluation, Forced Use, and Unwarranted Blame (Figure 1). 

Figure 1: A visual overview of AI Failure Loops. The six failure modes (in circles) that contribute to the dynamics of the AI Failure Loops exist within a web to illustrate the inter-connected relationship amongst the failure modes. 

These failures are not isolated problems, but arise from a broader tension: AI Failure Loops emerge from the confluence of overconfidence in the capabilities of AI systems and underconfidence in the skills and expertise of workers. Today, with recent waves of both AI hype and actual increases in AI capabilities, developers are targeting a rapidly expanding range of socially complex tasks across feminized and other devalued occupations. Drawing lessons from past AI deployments in these contexts, we argue that this may further accelerate gross underestimations of worker expertise and overestimations of AI capabilities. 

Importantly, nothing about the future of work is inevitable. By genuinely centering and uplifting workers in the design, evaluation, and governance of AI systems, we can begin to reverse these loops and develop AI that actually supports and dignifies, rather than diminishes, worker capabilities. Our paper ends with implications for shifting towards this future of pro-worker AI—a future where worker expertise is celebrated and uplifted through AI practice, and by society more broadly. 

1 Feminized labor is a particularly extreme example of devalued labor; historically, it has been mislabeled “women’s work,’’ and today, these jobs continue to be predominantly performed by women and people of color. Workers in these fields frequently report feeling undervalued and overworked, with many roles experiencing high retention rates. 

Call for Special Theme Proposals

   Call for Special Theme Proposals for Big Data & Society (Due August 31, 2026)

The SAGE open access journal Big Data & Society (BD&S) is soliciting proposals for Special Themes to be published in 2027. 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 on topics that the journal has not previously published extensively. 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 31, 2026, via this online form: https://forms.gle/3AT8vTZHskyEfD2i7

(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 late October 2026. For selected proposals, manuscripts would be submitted to the journal (via Manuscript Central) by or before February 28, 2027.

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

Guest Blog: Working Closely with Data: Aspirations and Constraints of Chinese Data Scientists

by Tingting Liu

Tan, J., & Liu, T. (2026). Working closely with data: Aspirations and constraints of Chinese data scientists. Big Data & Society, 13(1). https://doi.org/10.1177/20539517261424165 (Original work published 2026)

What drew me to studying data scientists was their unique position in digital economy. As a cultural anthropologist deeply engaged with labour cultures, I have spent years researching factory workers, livestreamers, white-collar employees, and police officers. When my co-author Jingxin Tan began her master’s project on digital labour, our interests converged on this newly emerging profession.

In both China and Ameria, this profession emerged around the same time, to a certain degree reflecting the intense technological competition between the two countries. In English, it is usually called “data scientist,” while in China it is widely known as “algorithm engineer”. On Chinese recruitment platforms, the two labels often appear side by side, and the scope of work they cover has been constantly shifting, reflecting how the role continues to evolve. Data scientists’ everyday work revolves around collecting and cleaning massive datasets, building and optimizing machine-learning models, running endless experiments, and translating algorithmic results into business decisions. In their own everyday language, they often jokingly describe this process as liandan (“alchemy”), capturing the sense of repeatedly tweaking parameters and hoping for breakthroughs.

Many data scientists know that the systems they build may replace other digital workers. Yet they often interpret this as proof that everyone must keep learning—or be left behind. This belief reflects how deeply they internalise the logic of the tech industry. At the same time, they joke about their own futures: “After 35, I’ll deliver food,” or “Maybe I’ll sell houses.” These jokes reveal both confidence and anxiety. What readers can take away is this: behind the glossy image of AI innovation are workers caught between high pay, professional pride, and fragile long-term prospects. Studying them helps us better understand the human costs of data-driven capitalism—and why optimism in tech can be both empowering and cruel.

Guest Blog: Rating villagers’ morality: techno-moral governance via a data scoring system in rural China

by Wilfred Yang Wang, Yu Sun, and Linlin Li

Sun, Y., Wang, W. Y., & Li, L. (2026). Rating villagers’ morality: Techno-moral governance via a data scoring system in rural China. Big Data & Society, 13(1). https://doi.org/10.1177/20539517261424163 (Original work published 2026)

With digital technologies applied in rural governance, everyday utilities and service delivery are digitised and datafied, creating new forms of order and structure of everyday practices in rural China. With the embeddedness of data, algorithms and platforms in every settings, governments strive to make automated and ‘smart’ technologies at the centre of its rural governance and social controls. 

This article develops a framework of techno-moral governance to capture the emerging forms of datafied governance in China. We take techno-moral governance as a critical approach to examine how infrastructural arrangements intersect with moral assessment in shaping governing techniques, practices and vision in and beyond rural China. 

Using the case of  the smartphone app, Xiangcun Ding, which is deployed in rural areas of Zhejiang province , we draw on fieldwork data collected during our ethnographic visits across 10 villages in Jiande county to explore the operation of the data scoring system of Xiangcun Ding as a national example of datafied rural governance. We tried to unravel how the state’s ideologies of governance and discourse of morality and civilization are built into the material characteristics and functions of technology and how the techno-moral governance operates on the basis of the embedded material-discursive nexus in rural China. 

Our argument is threefold. First, while the app operates on the seemingly automated algorithmic calculation to assess individuals’ morality, its deployment and operational efficiency rely on the ‘co-opting’ supervision between the administrative power of local governments and Alibaba’s techno-corporate supports. Second, the new data scoring system found its legitimacy from the socialist legacies of the work point (gongfen) system, which was practiced in Mao’s era, as part of its major functional features to encourage and mobilize uptake and acceptance of the new technology. Third, by immersing itself in local social infrastructures and everyday lives, Xiangcun Ding plays a critical role in normalising and justifying the datafication of lives and expanding the boundaries of social governance and control.  

The lens of techno-moral governance in rural areas allows us to shift our focus to reconfiguration of social material lives and relations through data technologies, beyond a liner focus on the construction of discursive politics, which has dominated our understanding of digital governance that focuses on urban China. Thus, we suggest that it is necessary to look at both the material and the discursive process and their composite to understand the complex dynamics of China’s internet governance and the emerging datafied approaches to governance.

Guest Blog: ChatGPT, a colonialist agent of lifeworlds: An Habermassian analysis of conversations

by Régis Martineau, Elise Berlinski, and Frantz Rowe

Martineau, R., Berlinski, E., & Rowe, F. (2026). ChatGPT, a colonialist agent of lifeworlds: A Habermassian analysis of conversations. Big Data & Society, 13(1). https://doi.org/10.1177/20539517261421473 (Original work published 2026)

The use of generative AI (GAI), such as ChatGPT, is becoming widespread and extending from professional to private life. Increasingly used as thinking aids, our relationship with them is shifting from simple tools to indispensable companions. To date, most publications on GAI analyze its productive capabilities. Questions arise about how it could increase employees' performance or solve previously unsolvable problems. However, its widespread use also implies profound changes in routine communicational interactions. 

According to Habermas, communication is constitutive of democracy, because through non-instrumental interactions we co-construct a world in accordance with common values. Drawing on Habermas’ theory of communication and an analysis of the properties of GAI, this article shows that interactions with GAI can only be instrumental. Indeed, GAI does not understand a conversation, but rather produces probabilistically probable sentences: it is a stochastic parrot. It cannot be trusted, as it is biased and its assertions change over time and are not coherent. It is unreliable and therefore not sincere. Furthermore, the origins of the biases cannot be identified, as the GAI is unaware of its judgmental foundations, as it does not understand a judgement.  

This has serious consequences for our democracies. Indeed, GAIs are gradually colonizing the space of co-constructive communication, which means that individuals increasingly struggle with non-instrumental communication. For example, it is much easier to have a romantic relationship with a GAI that can be tailored to one’s preferences. As a result, the availability and space for democratic conversation becomes reduced, and increasingly polarized. Furthermore, as this example also shows, they distort conversations and blur the line between the instrumental and the co-constructive. Worse, increasingly more studies report very limited productivity gains and even demonstrate negative cognitive effects. We call for drawing conclusions from this and limiting these tools to clearly delimited instrumental uses.

Guest Blog: Shaky Technology, Steady Momentum: How Generative AI Innovation Survives Setbacks

by Choroszewicz and Rannisto

Choroszewicz, M., & Rannisto, A. (2026). AI innovation at the boundaries: Justifying a generative AI decision support tool. Big Data & Society, 13(1). https://doi.org/10.1177/20539517261424159 (Original work published 2026)

Generative AI is entering the public sector under intense conditions: political pressure, organizational enthusiasm, and a widely shared conviction that innovation must move fast. Public organizations across Europe are experimenting at pace. These trials are often wrapped in familiar promises – greater efficiency and productivity, cost savings, better services for citizens, and relief from the friction of bureaucratic routines.

Our paper examines a generative AI decision support tool in Finnish public administration and shows how AI projects continue even when their promised outcomes remain unfulfilled. We found that the project was sustained through boundary-spanning practices and a powerful “package” of justification frames that made the tool appear irresistible across organizational and professional boundaries.

How a Technology Becomes Irresistible

We identified nine recurring justifications that together formed a protective structure around the tool’s development. This structure did two things at once: it kept the innovation moving from one experiment to the next, and it buffered the project against criticism when doubts about the tool’s reliability began to surface. We observed how these frames emerged and circulated through the project events, artefacts, and representations, making continued development appear well justified. Some of these frames drew their justificatory force from the imagined tool itself, while others drew on the conditions and practices that emerged around it.

The tool-oriented frames leaned on familiar AI promises – efficiency and cost savings – but also on claims about employee well-being and fairness for citizens, with desirability often functioning as a proxy for value. Around the tool, a set of process- and ideology-oriented frames cast speed, bold initiative, and experimentation as virtues, normalized setbacks and sustained innovation momentum.

Why It Matters Where Justifications Land

Justifications do not carry the same weight everywhere: what counts as a “good reason” depends on the institutional and cultural landscape in which it is received. In our paper, the Nordic welfare state context seemed to make certain appeals especially resonant, because they aligned organizational performance with worker protection and civic ideals.

A specific justificatory “package” stood out as particularly powerful, combining elements of (i) efficiency: faster, more consistent operation and decisions; (ii) employee well-being: reduced cognitive load for claims specialists; and (iii) civic fairness: fairer, more transparent, and more equitable outcomes for citizens.

Together, they formed a compact public-value package that travelled across groups and stakeholders, making continued development appear justified even though the tool’s performance remained limited.

Boundary Work: Alliances, Divides, and Shifting Responsibilities

Because the justification frames did not operate in isolation, their enactment and force also depended on boundary work, the practices through which organizational and professional lines were crossed, reinforced, or temporarily rearranged as the project unfolded. In other words, what could be justified, to whom, and on what terms was shaped by how relationships, roles, and resources were arranged around the tool.

Collaborative and configurational boundary work took the form of alliances with managers and consultants, alongside practical reconfigurations of existing boundaries to make experimentation possible. New meeting formats, shared artefacts, experiment arrangements, and reporting practices helped gather resources and attention around the tool and sustain its development.

Competitive boundary work surfaced most clearly at the interface between innovation and frontline work, a divide between the flexible world of innovation and the controlled routines, limited risk tolerance and evaluative standards of frontline work. At this interface, the central questions emerged: whose judgments carried weight in defining what the tool was, what it should do, and how its performance should be interpreted?

As the tool’s promises proved difficult to realize, responsibility for “making it work” increasingly drifted from the tool’s outputs toward organizational conditions, expectations, and patterns of use: user interaction, training, prompting practices, document formats, workflow changes, and “AI readiness” more broadly. This shift did not remove the tool’s technical limitations, but it changed where they were made visible – and where critique tended to land – recasting innovation success less as technical robustness and more as organizational and user transformation.

Failure as Business as Usual

The tool’s ongoing inability to deliver on its core promises did not bring the project to a halt. Instead, failure was often folded into the rhythm of innovation praxis as something to be anticipated, worked around, and learned from. Within this framing, failure became normalized as a default condition of a progressive innovation, a spur to further activity. Continuing uncertainty was taken as part of the work itself, and so further investments of time, attention, and resources appeared not only reasonable but necessary.

At the same time, the tool’s technical opacity made failure difficult to locate and therefore difficult to settle. When it is hard to say why a system fails, it is also hard to know what would count as a decisive reason to stop. Meanwhile, the surrounding hype around generative AI, combined with the rapid pace of language model development, made it plausible to expect that technical improvements would arrive “from the outside” as models matured. In our case, that expectation proved wrong several times. 

Why Some AI Projects Become Hard to Stop

Our paper shows that sustaining AI innovation is not merely a technical matter. It relies on ongoing boundary-crossing practices and on powerful justifications that resonate with shared values and organizational aspirations. Crucially, what matters is often not any single justification, but how certain justifications cluster into persuasive packages that fit the context in which they circulate.

Our papers also shows that the tool’s development persisted not because it met its promises, but because the surrounding justificatory dynamics made continuation seem reasonable and even difficult to interrupt. Such dynamics can generate momentum, mobilize attention, and direct resources. But they can also narrow the space for critical reflection – locking organizations into particular innovation trajectories and obscuring consideration of alternative pathways, including the option of pausing.