Tracing labour, power, and information in Artificial Intelligence Systems

Petter Ericson (AI Policy Lab, Department of Computing Science, Umeå University)


Published on 24 June 2025
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1. Introduction

It is common for technology to be used to obscure the role of humans, and artificial intelligence (AI)is a field where this is even more true than for many others. From ghost workers and data scraping to algorithmic management and automated decision making, AI technologies are used to displace, appropriate, and hide human labour in various ways. Decisions are hidden inside technical systems, externalising them from individuals and organisations who could be held meaningfully accountable, which can make agency and power flow in new and often poorly understood ways. By having automated systems imitate humans, teleworkers can be seamlessly swapped in and out as needed with users none the wiser, making the systems appear significantly more capable than they actually are.

A useful abstraction for studying and exposing the workings of these systems is to consider how and where information is flowing through them. However, tools for such analyses tend to either be highly abstracted and focused on the broader sociotechnical systems where AI components are situated, or highly technical and focused on the details of specific software and hardware architectures, or on idealized and abstract models thereof. The proposed work will attempt to bridge this gap. On the one hand, it will take a more rigorous approach grounded in information theory to qualify and quantify the information that the humans involved share both through some specific technical system and through outside channels. On the other hand, it will take a wider view on the concrete workings of those technical systems, incorporating sociotechnical metadata into analyses of digital information flows.

In sum, we aim to use tools and methods from information theory and sociotechnical system modelling, together with formal graph models and complexity theory, to investigate and explain how diverse human labour and knowledge is discretized, datafied, and expressed throughout the development and deployment of different types and architectures of AI systems.

A complementary aim of this work is to build on and further develop existing research connecting information, computation, labour, and value, and how these concepts interact, specifically in the context of AI systems, making concrete contributions to interdisciplinary studies on AI and data work. A second major aim is to yield new insights into how to quantify and qualify information flows, through a focused study of sociotechnical systems involving AI components, where information and its flows in the digital realm can be directly studied, and comparisons can be made to both models and empirical realities of the social realm surrounding them.

Ultimately, we aim to investigate the following research questions:

* RQ1 What types of information flows can be identified within different AI system architectures, and how can these be formally categorized?
* RQ2 How do human actors contribute to and engage with information flows in and around AI systems, and how can these social interactions be systematically modeled?
* RQ3 How can we develop and validate models of information flows in AI-based sociotechnical systems that integrate both technical and human components? * RQ4 How do modeled information flows reflect or reinforce particular organizational or institutional power structures?

2. Related work

Though to the best of the authors knowledge there is very little research on precisely the present topic, its interdisciplinary nature means that there are a number of intersecting areas of active research. In particular, works that cover the intersection between AI and information theory, between information theory and labour, between labour and AI, and between any of these three areas and sociotechnical modelling are all relevant.

For the first intersection, Jeon and Roy have recently investigated the connections between Bayesian machine learning and Shannon information theory, drawing an equivalence between the cumulative errors during a learning process of an optimal machine learning algorithm, and the amount of information contained in the data. From a different angle, several works such as Tseng et al. [2] and [3] have looked specifically at large language models (LLMs), drawing on compression and entropy calculations to study the use, and training, respectively of LLMs as related to natural language texts.

For the second, Dantas [4] has drawn direct connections between information and both labor and value in an explicitly Marxist framework, distinguishing between not only use and exchange value, but also semiotic value, and deriving a specific notion of information work which will be of direct use in the proposed work. Dantas also draws a distinction between random and redundant information work, which is similar to the distinction between semantic and syntactic information work in [5] which is further nuanced into an explicit spectrum in [6].

The third intersection is itself a broad area, with many different aspects of relevance. Nguyen and Mateescu [7] gives a good overview of the current landscape in relation to Generative AI specifically, while Davis [8] provides a broader review of relevant issues, making an explicit (and useful) distinction between cases where AI use impacts labour demand (through automation) and those relating more to worker power (through surveillance, algorithmic management, and the like). Further, works such as Crawford [9], Miceli and Posada [10], Gray and Suri [11], Merchant [12], Sadowski [13], and Mejias and Couldry [14] are all relevant for further developing this work. The Data Workers Inquiry (https://data-workers.org) will be another important source of alternate perspectives on AI and labour.

In terms of studying AI sociotechnical systems, once again several active areas are of interest. In particular Wu et al. [15] has developed a framework for integrating various types of models of sociotechnical systems (STS) into a single meta-model. Several modelling languages for sociotechnical systems exist, including STS-ml [16], which was developed for cybersecurity applications, and the host of standards and notations related to Business Process Model and Notation (BPMN), such as Decision Model and Notation (DMN), which is particularly relevant for models integrating AI decision support systems. However, all of these abstractions and models tend to integrate assumptions that are not always helpful for the purposes of this work. A relevant example of how existing modelling framework scan be extended to cover new areas is [17], which adds properties and functionality to STS-ml in order to check sociotechnical systems for compliance with the EU General Data Protection Regulation (GDPR).

A relevant parallel effort, though not directly related to the work we propose here, is that of Gutierrez Lopez and Halford [18], who aim towards an extension of XAI principles that including the sociotechnical environment of the machine learning system.

3. Aims

The main contribution of this work will be to integrate previous work on sociotechnical systems modelling with several notions of information and labour, specifically in the context of artificial intelligence. An additional benefit of this work will be to lay a basis for a further analysis of agency and accountability: By studying the information flows and potential inputs and decisions from humans involved in an AI sociotechnical system, together with an analysis of the power relations among them, accountability and responsibility can be transparently and meaningfully assigned.

We hope to make meaningful contributions to the practical use of information theory and information flow, as well as yield actionable and concrete directions for further exploration of new AI sociotechnical architectures. As part of this, a major component will consist of qualitatively and quantitively analysing the information flows into and out of AI and ML systems, which will also give new and useful insights into the design of Hybrid AI systems in particular.

By creating concrete tools and methods for tracing information flows through both technical and social layers of AI systems, this work will attempt to offer not just theoretical insight, but practical value for those developing, regulating, or critically analyzing such systems. In a time when the societal consequences of AI are increasingly opaque yet consequential, this research will provide actionable models that can inform transparency standards, system audits, and future AI governance efforts.

4. Preliminary results

4.1. Categories of information

A foundational topic of this work includes clarifying and classifying different types of information. In particular, though at extreme small ranges reality can occasionally appear to be digital, for most practical purposes, it is continuous. In contrast, digital information, and computer and AI systems, while implemented on physical hardware, are conceptually and practically discrete. As such, while abstractions of the human and physical sections and relations of a sociotechnical system are going to inevitably be lossy, for the digital parts it is in principle possible to be both precise and concrete. This then, must be our first distinction between fundamentally different types of information: Abstracted notions and models of the real world, and concrete digital bits and bytes.

In terms of different theoretical notions of information, we further contrast the more mathematical definitions of Shannon [19] (’minimal code’) and Kolmogorov [20] (’minimal program’) with the Batesonian concept of ’a difference which makes a difference’ [21]. A fourth relevant concept is Corning’s’ control information’[22], which rather than connecting Shannon entropy/negentropy directly to the physical thermodynamic concepts with the same name, instead quantifies the amount of information contained in some signal or phenomenon by the amount of physical changes that it can effect. An example taken directly from Corning [22] is that of a car approaching a stoplight. If the driver does not notice or understand the traffic light, there is no control information being transferred by whatever light is shown. Only if the driver both sees the light, understands it, and is prepared to change the future trajectory of the car, is there any control information being sent out by the light switching to red. Broadly, we can thus consider two very different types of information flows: The almost entirely discrete and abstract digital information exchanges between and inside of software components, and the messy, socially situated, and necessarily contingent and abstracted information flows that can be modelled to exist between humans, technological artefacts, and their surrounding physical context. The main interest of this work lies precisely where these information flows intersect and interact.

4.2. Human-computer information interactions

With a minimal distinction of information flows as above, consider the interactions between a human and a computer system: the transfer of information from human to computer will necessarily abstract some concrete intention of the human into a concrete digital signal, but likewise a (digital) computer output will take on a specific meaning to the human which depends on their prior knowledge and the context in which the output is given. We can depict these shifts as in figure 1.

4.3. Analysis example

As an illustrative example of the type of analysis that we aim to make more concrete, detailed, and empirically grounded, consider the case of an article being written about a sports event. It is, at this point, plausible that such an article could be written by a large language model (LLM) given an appropriate prompt, including some sort of summary of the ’relevant facts’ of the event in question (e.g. the final tally of points, who made them when, and any injuries and other specific incidents, which are accessible from some sort of API). The situation would look something like figure 2. We can complicate this picture, however, by adding more context. The article will not reach publication without an editor, and the hidden labour that has resulted in the LLM is entirely absent in our initial figure, as is the work to set up the “sports API” and the later work to feed it with the ’relevant facts’ from observations of the event. A more realistic picture emerges, as in figure 3.

Compare this to a situation where a human writer is the author of the same article. Though the plain (abstract) facts of the event in question may be the same, the human will also have access to an infinitely larger context as part of their writing process, both through direct experience and memory, and through communications with other humans, computer systems, and physical objects such as books and recordings, nevermind the sights, sounds, and smells of the event itself if the writer was also present at the event. In this case, the situation will look more like figure 4. This too can be made more complex, particularly if we imagine the writer to make use of an LLM for writing assistance of some sort, yielding a situation as in figure 5.

5. The path forward

Though primarily based in computing science, the nature of the problems addressed by the work call for an interdisciplinary approach. Notably, by building on existing work in Science and Technology Studies, as well critical marxist literature, it is possible to better situate and analyse the information flows and AI sociotechnical systems inside existing societal power structures and socioeconomic realities. In addition to the various theories of information mentioned in Section 4.1, we will also distinguish between different types of information work, as outlined in Section 2. The distinctions between data, information, and knowledge have been explored e.g. in [23], [24], and these perspectives will also be considered.

We will primarily be building on existing frameworks for the analysis of program structure and information flow through software. Notably, the theory and practice of Quantitative Information Flow (QIF) analysis in computer security, though focused on detecting and plugging information leaks between public and private variables under static source code analysis conditions, offers a range of useful tools for modelling intentional information flows as well. From a software engineering lens, constructing program flow graphs and clearly and consistently delineating components in a software system is a well established practice, with a host of frameworks available for use. An example of an abstract framework for describing program and information flows that has been specifically developed for purposes of describing Hybrid AI systems is the boxology of Harmelen and Teije [25].

For situating a software system in an organisational context, tools from business modelling are available as well, with well-established frameworks such as BPMN, STS-ml and various derivatives having seen extensive use to analyse information flows and decision processes in business contexts. Concretely, the near parts of this work will consist a phase of conceptual and theoretical grounding, studying and comparing existing frameworks for information flow analysis, to arrive at a rigorous and flexible framework for modelling information flows in sociotechnical systems, incorporating the above distinctions and specificities, and giving specific attention to questions of agency and valuation. This work will aim at identifying connections and distinctions in how different frameworks frame decisions, and how labour is considered within them.

In the course of this development, a metadata schema for information and information flows will be developed that can describe and categorise information both in terms of its qualities, its different information contents, as well as its role at a specific point in a described sociotechnical process. Tracing the changes of these properties as attached to a particular piece of information will be an important complement to the analyses of the flows themselves, and of the various transformations imposed on and driven by the information.

During and after these developments, the framework and schema will be empirically applied to real-world cases, both existing ones from the literature, and new and comparable studies of previously understudied sociotechnical contexts. Modelling these flows will be accomplished through direct study of technical artefacts and their documentation, as well as organisational policies and descriptions of their surrounding sociotechnical contexts. These will be supplemented by interviews and surveys of involved stakeholders to elicit new and undocumented perspectives not previously represented even in internal documents.

Through comparative analysis across multiple cases (to be selected to reflect diversity in AI architecture and deployment and across different domains, e.g. public-sector automation, language models, decision-support tools) the framework will be further refined to capture how different system configurations mediate flows of information, labour, and power across different AI configurations. Ultimately, we aim for a formal, extensible modelling framework for analyzing information flows in sociotechnical systems involving AI, as well as a richly annotated library of concrete case studies. Additionally, we aim to make both conceptual and methodological contributions to the study of accountability, power, and labour in AI, as well as help drive further developments in related fields.

References

[1] H. J. Jeon, B. V. Roy, Information-Theoretic Foundations for Machine Learning, 2024. URL: http://arxiv.org/abs/2407.12288. doi:10.48550/arXiv.2407.12288, arXiv:2407.12288 [stat].

[2] Y.-H. Tseng, P.-E. Chen, D.-C. Lian, S.-K. Hsieh, The semantic relations in LLMs: An informationtheoretic compression approach, in: T. Dong, E. Hinrichs, Z. Han, K. Liu, Y. Song, Y. Cao,C. F. Hempelmann, R. Sifa (Eds.), Proceedings of the Workshop: Bridging Neurons and Symbolsfor Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LRECCOLING-2024, ELRA and ICCL, Torino, Italia, 2024, pp. 8–21. URL: https://aclanthology.org/2024.neusymbridge-1.2/.

[3] M. Yin, C. Wu, Y. Wang, H. Wang, W. Guo, Y. Wang, Y. Liu, R. Tang, D. Lian, E. Chen, EntropyLaw: The Story Behind Data Compression and LLM Performance, 2024. URL: http://arxiv.org/abs/2407.06645. doi:10.48550/arXiv.2407.06645, arXiv:2407.06645 [cs].

[4] M. Dantas, Information as Work and as Value, tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 15 (2017) 816–847. URL: https://www.triple-c.at/index.php/tripleC/article/view/885. doi:10.31269/triplec.v15i2.885.

[5] J. Warner, Labor in information systems, Annual Review of Information Science and Technology39 (2005) 551–573. URL: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/aris.1440390120. doi:10.1002/aris.1440390120.

[6] J. Warner, The spectrum of semantic and syntactic labour, Journal of Documentation 80 (2024)649–664. URL: https://www.emerald.com/insight/content/doi/10.1108/JD-03-2023-0057/full/html.doi:10.1108/JD-03-2023-0057.

[7] A. Nguyen, A. Mateescu, Generative AI and Labor: Power, Hype, and Value at Work, Technical Report, Data & Society Research Institute, 2024. URL: https://datasociety.net/library/generative-ai-and-labor. doi:10.69985/gksj7804.

[8] O. F. Davis, Artificial Intelligence and Worker Power (2024).

[9] K. Crawford, Atlas of AI: power, politics, and the planetary costs of artificial intelligence, Yale University Press, New Haven London, 2021.

[10] M. Miceli, J. Posada, The Data-Production Dispositif, Proceedings of the ACM on Human-Computer Interaction 6 (2022) 1–37. Publisher: ACM New York, NY, USA.

[11] M. L. Gray, S. Suri, Ghost work: how to stop Silicon Valley from building a new global underclass, Houghton Mifflin Harcourt, Boston, 2019.

[12] B. Merchant, Blood in the machine: the origins of the rebellion against big tech, first edition ed., Little, Brown and Company, New York, 2023. OCLC: on1389775757.

[13] J. Sadowski, The mechanic and the luddite: a ruthless criticism of technology and capitalism, University of California Press, Oakland, California, 2025. doi:10.1525/9780520398085.

[14] U. A. Mejias, N. Couldry, Data grab: the new colonialism of big tech and how to fight back, WHAllen, London, 2024.

[15] P. P.-Y. Wu, C. Fookes, J. Pitchforth, K. Mengersen, A framework for model integration and holistic modelling of socio-technical systems, Decision Support Systems 71 (2015) 14–27. URL: https://www.sciencedirect.com/science/article/pii/S016792361500007X. doi:10.1016/j.dss.2015.01.006.

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[17] C. Negri-Ribalta, R. Noel, N. Herbaut, O. Pastor, C. Salinesi, Socio-Technical Modelling for GDPR Principles: an Extension for the STS-ml, in: 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW), 2022, pp. 238–243. URL: https://ieeexplore.ieee.org/document/9920163/?arnumber=9920163. doi:10.1109/REW56159.2022.00052, iSSN: 2770-6834.

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[20] A. N. Kolmogorov, On Tables of Random Numbers, Sankhy?: The Indian Journal of Statistics, Series A (1961-2002) 25 (1963) 369–376. URL: http://www.jstor.org/stable/25049284, publisher: Springer.

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[22] P. A. Corning, Control information theory: the ‘missing link’ in the science of cybernetics, Systems Research and Behavioral Science 24 (2007) 297–311. URL:https://onlinelibrary.wiley.com/doi/abs/10.1002/sres.808. doi:10.1002/sres.808, _eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1002/sres.808.

[23] L. Businska, I. Supulniece, M. Kirikova, On Data, Information, and Knowledge Representation in Business Process Models, in: R. Pooley, J. Coady, C. Schneider, H. Linger, C. Barry, M. Lang (Eds.),Information Systems Development, Springer, New York, NY, 2013, pp. 613–627. doi:10.1007/978-1-4614-4951-5_49.

[24] L. Businska, I. Supulniece, Towards Systematic Reflection of Data, Information, and Knowledge, Scientific Journal of Riga Technical University. Computer Sciences 43 (2011). URL: https://content.sciendo.com/doi/10.2478/v10143-011-0002-9. doi:10.2478/v10143-011-0002-9.

[25] F. v. Harmelen, A. t. Teije, A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems, Journal of Web Engineering 18 (2019) 97–124. URL: http://arxiv.org/abs/1905.12389.doi:10.13052/jwe1540-9589.18133, arXiv:1905.12389 [cs].

Keywords (comma separated):

information theory, information flow, socio-technical system modelling

Related URL (if any):

https://people.cs.umu.se/~pettter/tracing_information_figures.pdf

How to cite this article:

Ericson P. (2025). Tracing labour, power, and information in Artificial Intelligence Systems. AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/AUHD8541

Time Out of Joint: Historical reflections on AI

Somya Joshi (Stockholm Environment Institute) & Remi Paccou (Schneider Electric)


Published on 27 May 2025
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Artificial Intelligence. The very term conjures images of futuristic robots and sentient machines for some, and images of climatic collapse and existential risk to others. This AI hype represents a disjoint in time with both risks and promises. It signals a paradigm shift marked by unprecedented capabilities in information processing, autonomous reasoning, and pattern recognition, challenging traditional notions of progress and sustainability while demanding a nuanced approach to harness its potential responsibly & ethically.

The Three Technological Paradigms: From water wheels to apps:

Human technological evolution can be understood through three major paradigms. The first focused on the transformation of materials, spanning from the Stone Age through the Bronze and Iron Ages(1), where humans developed increasingly sophisticated ways to manipulate their physical environment. The second paradigm, also known as the Industrial Revolution, centered on the transformation of energy. The first industrial revolution (1770–1850), as identified by Schumpeter(2), was driven by water-powered mechanization, including mills and irrigation systems. The following long wave (1850–1900) was enabled by steam-powered technology, revolutionizing transportation with trains and transforming industrial machinery. Around 1900, the Third Kondratieff Cycle began(3), marked by the electrification of society and production from 1900 to 1940. Each revolution introduced new tools, industries, and fundamentally impacted lifestyles.

Today, we stand at the cusp of an era defined by the transformation of information. Late 20th-century digital electronics fueled ICT digitalization, leading to AI disruption. But what does this disjoint in time truly entail? History reveals three fundamental mechanisms that have been central to major technological transitions: transmission, storage, and processing. These mechanisms have propelled every major technological shift: from the wheel and rope of transport to smoke signals and the internet for transmission; from containers and reservoirs to photography and magnetic media for storage; and from fire-making to electronic computation for processing(4).

In 1990, less than 0.05% of the global population used the internet. By 2020, over 59% of humanity was connected (10). Networks now move exabytes monthly, enabling unprecedented global information flow. Storage has mirrored this progression: from physical media like books, we’ve advanced to digital systems that store humanity’s collective knowledge on infinitesimal footprints—a leap from 1% digital in the late 1980s to 99% by 2012. AI compute has completed the picture, with processing power showcasing the most striking leap forward. Today’s supercomputers operate at exaflop speeds, solving in seconds problems that would take humans decades. These leaps in transmission, storage, and processing power form the bedrock of AI, enabled by infrastructure that facilitate information transmission, storage, and processing at unprecedented levels. However, these accelerations come at a cost – both to human societies and the planet.

Continuity and Discontinuity in AI Development

Unlike past technologies that built upon human abilities, AI promises autonomous reasoning, planning, and pattern detection beyond human limits. This shift, especially with the rise of agentic AI systems, challenges traditional augmentation concepts, introducing self-referential mechanisms that redefine intelligence, creativity, and technological agency.

This transformation can be framed through the concept of autopoiesis, where technological systems evolve to create themselves, or sympoiesis, where AI is built upon human knowledge to enable novel futures(6). These theoretical lenses help us understand not only the abstract nature of AI’s development but also its tangible manifestations in the evolution of computational hardware. While computational hardware has experienced profound changes, marked by incremental efficiency gains and increased capabilities, the nature of AI’s advancements, particularly its generative capacity, introduces a new dimension. AI’s generative capacity, as it currently stands, challenges human cognitive boundaries and increases technological opacity, introducing a fundamental break from previous technological trajectories. It is not merely an extension of human capabilities but a transformative force capable of generating insights and futures untethered from human precedent.

Untethered from Planetary Health: Rebound Effects and Sustainability Challenges

Current research warns of potential “rebound effects,” where gains in efficiency paradoxically lead to higher overall consumption—an abundance without limits that could undermine sustainability goals by constraining decarbonization efforts or generating waste through unrestricted growth in AI development(7). Addressing this requires policy interventions and investments in sustainable infrastructure prioritizing accuracy, frugality, proven impact assessments for electricity demand growth—and circular economy practices for both hardware and software. To align AI development with planetary (and by virtue of that human) resilience, guardrails need to be designed within the architecture of AI technologies, at the very heart instead of as an afterthought. This would entail a shift away from a focus on efficiency and optimisation alone, towards a more integrated perspective that considers the entire value chain of AI(8). Furthermore, the environmental impact of AI, including the energy & water consumption of large language models and resource depletion from hardware production, must be addressed via caps and transparent open architecture for data sharing.

From Extraction to Global Common: Resetting AI Development

Another critical discontinuity stems from historical notions—dating back to early industrial revolutions—that “human progress” exists outside of nature, which then reduces our environment to a resource for extraction. Today’s dominant discourse around scaling larger AI models risks perpetuating this extractive mindset despite rising environmental costs like energy & water crises caused by mismatched demand on infrastructure or resource depletion.

We call for a “Global Commons” approach (drawing on the seminal work of Elinor Ostrom(9), which would mean sharing benefits across borders while challenging protectionist development paradigms through sustainable practices. This includes optimizing software, improving models, evaluating environmental impacts, and promoting circular economies. We must also in parallel build global governance, set AI standards, and boost digital literacy through international collaboration. The fundamental question remains: when not to use AI? In other words, we must dare to imagine futures with and without AI, rather than accept it as a fait accompli.

To responsibly leverage AI, we must center its design and direction towards nature aligned principles, address potential risks and harms head on, and foster global collaboration in the face of an increasingly polarizing world. Sustainable strategies require long-term vision, while short-term profits shackle us to false promises of shared progress, which history reveals to be mere mirages.

References

  1. Roos, R. A. (2019). The Stone Age, Bronze Age and  Iron Age Revisited. HISTO THOMSEN V2-. J. Terrestrial Electrostatics.
  2. Schumpeter, J. A. (1939). Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. McGraw-Hill.
  3. Korotayev, A. V., & Tsirel, S. V. (2010). A Spectral Analysis of World GDP Dynamics: Kondratieff Waves, Kuznets Swings, Juglar and Kitchin Cycles in Global Economic Development, and the 2008–2009 Economic Crisis. Structure and Dynamics, 4.
  4. Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. Simon and Schuster.
  5. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living. D. Reidel Publishing Company.
  6. Haraway, D. J. (2016). Staying with the trouble: Making kin in the Chthulucene. Duke University Press.
  7. Paccou, R., & Wijnoven, F. (2024). Artificial intelligence and electricity: A system dynamics approach. ResearchGate.
  8. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University 1 Press
  9. Ostrom, E., Burger, J., Field, C. B., Norgaard, R. B., & Policansky, D. (1999). Revisiting the commons: local lessons, global challenges. science, 284(5412), 278-282.
  10. “Data Page: Share of the population using the Internet”, part of the following publication: Hannah Ritchie, Edouard Mathieu, Max Roser, and Esteban Ortiz-Ospina (2023) – “Internet”. Data adapted from International Telecommunication Union (via World Bank). Retrieved from https://ourworldindata.org/grapher/share-of-individuals-using-the-internet [online resource]

Keywords:
Artificial Intelligence, Sustainability, Geopolitics, Environment, Automation, Equity

How to cite this article:

Joshi S. (2025). Time Out of Joint: Historical reflections on AI. AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/DNPK4001

Will university teachers become obsolete in times of AI?

Elin Kvist (Department of Sociology, Umeå University)


Published on 22 May 2025
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We live in times of endless crisis alarms, a general state of uncertainty, combined with an orchestrated and intentional undermining of established academic institutions. Science and academic research are under questioning around the world, and universities’ position as a legitimate source of knowledge and critical thinking are under attack. Adding to this sense of instability and uncertainty, there is an ongoing digital and technological development that some argue has become a major risk for technical unemployment – fortelling the “end of work” (Brynjolfsson & McAfee, 2014; Danaher, 2017).

Automation, history and current threats

Historically, we have seen how new technology has changed the way we work and our everyday work practices. Machines have revolutionised and increased efficiency in agriculture and industrial production, and reduced the number of workers demanded in the process. In light of today’s ever-growing improvements in computer power, artificial intelligence and robotics, the gloomiest predictors are now once again convinced that we are moving towards a jobless future (Brynjolfsson & Mcafee, 2014). This time, technology is substituting more cognitively advanced and emotionally demanding jobs – ones previously performed by professionals in technical and managerial professions, including university teachers (Autor, 2015).

However, the past two centuries of automatisation and technological change have not made human labour obsolete. Even though unemployment rates have fluctuated cyclically, there has been no long-run increase in unemployment (Autor, 2015). Through governmental programs of re-education and reorientation, most exempted workers have been able to move to other forms of labour, and new areas of work have opened up in the wake of technological transformation. In previous technological automation processes the focus have been on replacing the most dangerous, bodily harming, and repetitive tasks, and in doing so contributing to a more human friendly labour market.

The digital transformation of academic work

University teachers have seen their work tasks and everyday work practices changed dramatically with digitalisation. Through digital aids and tools, they book rooms, coordinate and organise lectures and seminars, examine and grade students digitally, do research, apply for research funding and ethical approval, publish in academic outlets, manage conference bookings, calculate budgets for research proposals and develop data management plans. These are just a few examples. Their everyday work practices include a significant amount of digital administration, interacting with large numbers of different digital platforms. In the name of efficiency, an increasing number of tasks that have been previously assigned to administrative employees, have gradually been reassigned to the university teachers.

However, when trying to understand the consequences of technological changes of the everyday work situation for university employees, it is important to also take into consideration that during the same time these organisations have also been subject to New Public Management (Thomas & Davies, 2002). Which has also entailed increases in the number of students with diverse needs, less preparation time for teaching, and continuous monitoring of performance through audits and performance evaluations. In result, this left each university teacher with less time to do research due to increased demands and shrinking resources. Consequently, this has spurred even more administrative work as researchers constantly need to apply for research grants in highly competitive, complex and time-consuming funding processes, resulting in additional time and resource-consuming processes. This is also important to take into consideration when trying to understand the implications of genAI on the future of university teachers, illustrating the importance of moving beyond a techno-deterministic understanding (Lindberg et al., 2022). Automation and digitalisation are often presented as neutral, a consequence of technological progress, and as something inevitable. The ideological and material consequences remain hidden (Lindgren, 2024).

AI’s role and data dependency

We have to understand what distinguishes AI technology from previous types of technology, and in doing so, understand what consequences it will have on the everyday practices of university teachers. First, how can they use AI in their everyday work? What tasks do they have that could be suitable for genAI tools? Tasks such as compiling large amounts of text or getting an overview of a new research field for teaching or research, writting summaries, and polishing research applications, compiling CVs and creating concise bios, conducting  text analysis of ethnographic materials, supporting peer-review and expert processes, when assessing exams and essays, supporting development of lectures, conference and seminar presentations. The possibilities are endless. In the modern universities that the New Public Management have constructed, with its endless rounds of evaluations, constant applications and assessments, genAI tools can function to support and facilitate the everyday administrative work, making the work practices more manageable. However, it is also important to keep in mind that AI needs large amounts of data to be able to learn from the environment in which it will operate. To be able to help the teachers in their professional practices, genAI needs access to information and data, and the employees must assist and train the systems. Algorithmic systems depend on humans performing a certain kind of digital work, data labeling and moderation, breaking down the work into smaller components for autonomous decisions (Lindgren, 2024). This work is not always visible or even seen as actual work (Moore & Woodcock, 2021).

Hidden labour and digital capitalism

In digital capitalism, we all are involved in generating this type of data. When we move around in digital environments, we perform a lot of work for free that contributes to generating profits for the system, often without us being aware of it. As users we contribute to training the AI systems. Those who are involved in creating content online leave behind data traces, and it is these traces that the large digital media giants (Meta, X, etc.) exploit and capitalise on. The work that people do in the borderland between AI and society is often hidden (Lindgren, 2024; Moore & Woodcock, 2021; Taylor, 2018). For example, when you order a pizza for home delivery via an app, you might perceive it as a digital process. However, the actual physical work behind is invisible. Someone is standing and making the pizza. Another person is delivering it to your home. Foodora and Deliveroo’s apps are part of the complex socio-technical ecosystem of digital society. The pizza delivery people use their own bicycles to deliver the pizza. Foodora does not own the bicycles. Therefore, the company is not responsible for them. The companies claim to offer “flexible and free work”. The couriers can work whenever they want. The work is clearly fragmented, and the workers are interchangeable. The work schedule is individualised. The workers have to deal on their own with all the challenges, including icy roads, angry customers, unclear directions, and other issues. The digital platforms pay for the result, not for the time in-between. In many ways, these working conditions resemble those at the beginning of industrialisation, before union mobilisation, labor protection, sick leave pay, and the right to vacation (Ilsøe & Söderqvist, 2023). What is presented as high-tech and new, is in fact a regression in labour law. Historically, we have seen how every technological leap favors the emergence of armies of marginalised workers, who would take jobs that are not considered jobs anymore. In this respect, automation processes are often much less impressive than the big tech companies and large digital platforms want us to believe. Some tasks may disappear and wages will be reduced, though people continue working alongside the machines for lower pay or even sometimes without pay (Taylor, 2018).

To understand work under digital capitalism, we need to go back to the basic question formulated within  the socialist feminist tradition “What is work?” (Ferguson, 2020). How digital capitalism has not only survived but prospered while certain types of work have been hidden and unpaid (Fraser, 2016). The unrecognised work performed by most of us in the borderline between genAI and society, mirrors digital capitalism’s historical and current approach to reproductive work (Jarrett, 2018). The indispensable affective and material labour, mostly cast as “women’s work”, often performed without recognition and pay. In other words, a work that is not regarded as work and is not considered as having any social or economic value. This work in practice is extremely important, while ideologically seen as completely unimportant. Departing from this reasoning, we can conclude that the capitalist system have an inherent desire to devalue and hide socially important work. As participants of the digital capitalism, we often ignore the work that takes place behind the applications, and buy the myth of “success”. This way, we give automation more credence than it deserves. We ignore the work that lies behind the shiny facades of digitalisation. Making the machines appear smarter than they are (Taylor, 2018).

If the discussion about technology continues to focuse only on the narrative that technology drives humanity’s development forward and humans have to keep up, there is an imminent risk of missing the social contexts in which these technical devices are created. When considering the consequences of genAI on university teachers and their daily work, it is important to understand that it is not the technology that will make the teachers obsolete. Technology is developed within a specific economic and social system, where certain resourceful organisations and individuals invest in developing technology that will benefit their personal interests, including control, power, and immense financial returns.  The technology is designed to replace human labor to some extent, but it is developed within a digital capitalism that thrives on making people feel that they are constantly replaceable and vulnerable.

Conclusion and final reflections

To conclude, will genAI make university teacher obsolete? There is a need to acknowledge both the advantages and disadvantages of these tools. With the current university climate orchestrated by the New Public Management with its constant demands for auditing, evaluations, counting, and compiling information, daily tasks of university teachers might become more manageable with the help of genAI. In a better of world, genAI tools might be used to feed the insatiable New Public Management system, freeing up time for research, ciritical thinking and teaching. On the other hand, AI needs data and other resources to be able to learn from the environment in which it will operate. When university teachers participate in training the algorithmic systems, the digital capitalism thrives. This work is often not recognised as work. Digital capitalism wants us to believe that technological development is unstoppable and that we need to accept that our work is exploited. As educators and citizens, we need to be aware that there is an inherent mechanism in the system that actively benefits from hiding work tasks and treats them as non-work.

References

Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

Danaher, J. (2017). Will Life Be Worth Living in a World Without Work? Technological Unemployment and the Meaning of Life. Science and Engineering Ethics, 23(1), 41–64. https://doi.org/10.1007/s11948-016-9770-5

Ferguson, S. J. (2020). Women and work: Feminism, labour, and social reproduction. Between the lines.

Fraser, N. (2016). Contradictions of Capital and Care. New Left Review, 100, 99–117.

Ilsøe, A., & Söderqvist, C. F. (2023). Will there be a Nordic model in the platform economy? Evasive and integrative platform strategies in Denmark and Sweden. Regulation & Governance, 17(3), 608–626. https://doi.org/10.1111/rego.12465

Jarrett, K. (2018). Laundering women’s history: A feminist critique of the social factory. First Monday. https://doi.org/10.5210/fm.v23i3.8280

Lindberg, J., Kvist,E., & Lindgren, S. (2022). The Ongoing and Collective Character of Digital Care for Older People: Moving Beyond Techno-Determinism in Government Policy. Journal of Technology in Human Services, 40(4), 357–378. https://doi.org/10.1080/15228835.2022.2144588

Lindgren, S. (2024). AI – ett kritiskt perspektiv (Upplaga 1). Studentlitteratur.

Moore, P. V., & Woodcock, J. (2021). Augmented Exploitation: Artificial Intelligence, Automation, and Work. For Work / Against Work. Pluto Press. https://onwork.edu.au/bibitem/2021-Moore,Phoebe+V-Woodcock,Jamie-Augmented+Exploitation+Artificial+Intelligence,Automation,and+Work/

Taylor, A. (2018). The Automation Charade. Logic(s) Magazine. https://logicmag.io/failure/the-automation-charade/

Thomas, R., & Davies, A. (2002). Gender and New Public Management: Reconstituting Academic Subjectivities. Gender, Work & Organization, 9(4), 372–397. https://doi.org/10.1111/1468-0432.00165

Keywords:

Technological unemployment, genAI, work, gender, digital capitalism, university teachers, reproductive work

How to cite this article:

Kvist E. (2025). Will university teachers become obsolete in times of AI? AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/CBGU8049

AI Technologies in Public Service: A Workshop for Identifying Needs, Challenges, and Solutions


Date & Location: April 9, 2025, Umeå University, Västerbotten, Sweden

The workshop was organised by the AI Policy Lab in collaboration with the AI Technologies for Sustainable Public Service Co-creation (AICOSERV) project members.

Overview

The workshop brought together more than 50 stakeholders from the public and private sectors, as well as academia, to explore the relationship between barriers to AI adoption in public services and the skills and expertise required to overcome them.

A central theme of the workshop was the “question zero”, the fundamental inquiry of whether AI should be used at all in a given context. As AI technologies continue to advance and expand into complex public sector tasks, the assumption that AI is always the right or necessary solution must be critically examined. The workshop challenged participants to consider not only how AI can be implemented, but to question whether it should be, emphasizing that responsible adoption begins with questioning the appropriateness and desirability of AI in specific domains.

This foundational concern set the tone for broader discussions about trust, governance, transparency, and the skillsets required to navigate the opportunities and risks of AI in public service.

Keynote Address

Professor Virginia Dignum, Director of the AI Policy Lab, opened the workshop with a keynote titled “Governing AI: Why, What, How?”

She addressed the societal and governance implications of AI, focusing on the need to critically evaluate when and how AI should be integrated into public service contexts. Her talk stressed the importance of not overlooking ethical, legal, and operational challenges in the rush to adopt AI.

Regional Case Study: AI in Västerbotten

Considering the wide range of public services open to AI adoption, a recurring set of challenges consistently emerges. Whether deploying AI-driven diagnostic tools in healthcare or implementing predictive analytics within smart city infrastructures, public and private sector actors, and community stakeholders face diverse barriers. Henry Lopez-Vega, fellow at the AI Policy Lab, presented on the challenges of AI adoption in the Västerbotten region in his session titled “What are the challenges with AI (in Västerbotten)?”

His research identified three core barriers to building a responsible AI ecosystem:

  • Technological infrastructure and processes within organisations
  • Organisational culture and resistance to change
  • Lack of clarity around AI governance and ownership

Group Discussions: Skills and Stakeholder Engagement

In the second half of the workshop, participants engaged in group discussions focusing on organisational challenges, key stakeholders, and barriers to implementation. Each group then mapped the skills and knowledge needed for responsible AI adoption in their contexts.

For example, in the case of AI-supported recruitment processes, participants identified several critical barriers:

  • Lack of transparency in AI decision-making
  • Biases in training data
  • Limited legal and ethical guidelines for automated hiring

To address these issues, participants emphasized the need for professionals with a blend of competences, including:

  • Knowledge of data protection and anti-discrimination legislation
  • Skills in evaluating and auditing AI systems
  • Awareness of ethical considerations in algorithmic decision-making

Findings and Framework

The increasing efforts to implement AI across various public sector domains have led to a critical question: what types of professionals, equipped with what specific skills and competences, should lead the integration of responsible AI? Defining the essential set of skills, knowledge, and professional competences required for the effective and ethical deployment of AI in both public and private sector services becomes a key priority.

A key outcome of the workshop was the identification of a recurring set of challenges affecting. These include:

  • Low levels of trust
  • Lack of transparency
  • Unclear ownership and responsibility
  • Insufficient stakeholder awareness
  • Limited AI literacy and governance skills

To address these challenges, we propose the conceptual framework depicted in Figure 1. This framework highlights the urgent need to cultivate professionals who combine technical expertise, ethical sensitivity, and domain-specific knowledge to lead responsible AI integration. It maps the interconnected layers that influence the deployment and responsible use of AI technologies in public services, including:

  • Contextual Challenges – such as low trust, resistance to change, and limited organisational readiness
  • Structural Barriers – including unclear project ownership, inadequate governance frameworks, and insufficient infrastructure
  • Skill and Knowledge Gaps – highlighting the lack of AI literacy, ethical awareness, and domain-specific competences
  • Stakeholder Roles – outlining the importance of identifying and engaging relevant actors (e.g. policymakers, IT professionals, legal advisors, and citizens) throughout the AI lifecycle

This framework is intended to guide structured reflection and planning around AI deployment, helping ensure that technologies are not only functional but also trustworthy, inclusive, and aligned with public values. As such, it can serve as a practical tool for organisations seeking to integrate AI technologies responsibly. It encourages a systemic perspective, one that moves beyond technical feasibility to consider broader organisational, social, and ethical dimensions.

By applying this framework, decision-makers and project leads can:

  • Identify context-specific challenges before adopting AI
  • Map key stakeholders and clarify roles and responsibilities
  • Recognise skill and competence gaps that must be addressed
  • Design AI initiatives that align with principles of transparency, accountability, and fairness

Figure 1. The framework summarising the workshop’s thematic discussions

In conclusion, the workshop underscored that a stakeholder-oriented, challenge-driven approach is key to enabling responsible AI adoption. By starting with specific domain needs and mapping corresponding skills and knowledge, organisations can more effectively navigate the complex landscape of AI integration.

Civil Sector Vulnerabilities and NATO’s Strategic Role: The Case for International AI Governance

Jason Tucker (Researcher, Institute for Futures Studies, Sweden. Adjunct Associate Professor, AI Policy Lab, Department of Computing Science, Umeå University)


Published on 14 May 2025
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Adapted from a presentation given at the NATO Science for Peace and Security Programme, Advanced Research Workshop “Clicking the Pause: The Role of Transatlantic Cooperation in AI Supervision”, Salamanca, Spain, 8-9 May 2025.

As AI becomes increasingly embedded in critical societal functions, the need for robust, internationally coordinated governance grows more urgent. While some national and regional regulation of AI is emerging, applications in defence and international security often remain exempt from these initiatives. This historical separation between civil and defence sectors is understandable given the unique operational requirements of the military. However, it risks creating a false dichotomy—suggesting that AI use in civil domains is largely divorced from international security concerns. However, the geopolitical implications of AI in the civil sector are profound and escalating (Schaake, 2024).

To illustrate this, healthcare provides a concrete and urgent example. Across NATO members and partners, localized and largely disconnected decisions are being made to adopt small-scale AI solutions in healthcare. With states having limited capacity to develop in-house solutions, they often turn to external actors. Doing so means that they are then subject to a complex and opaque web of global supply chains and international actors. This poses substantial risks, including vulnerabilities to cyber-attacks, dependencies on potentially hostile states or corporations, and strain on critical infrastructure to support its adoption.

The growing instability of the international order compounds these challenges. The United States has recently exhibited unpredictability in both its Administration and its corporate tech sector. Even if diplomatic relations are maintained, trust at the local level is harder to rebuild. Working with partners whose long-term reliability is in question introduces significant risk, and other non-traditional partners become more appealing. Where these actors are not aligned with NATO, this could be a vulnerability.

Moreover, the adoption of AI in the civil sector has been driven by techno-solutionism — the prioritisation of technological fixes that neglects broader societal and security trade-offs, as well as potentially more appropriate non-technical solutions. It glosses over the reality that AI, as a socio-technical system is embedded in cultural, institutional, and ethical contexts and requires participation from a broad range of actors to function at its best.

Healthcare systems are particularly susceptible to this narrative (Strange and Tucker, 2024). They face resource constraints that limit the capacity to develop, implement, and secure AI technologies. Combined with the dominant discourse being that AI is the only and best way to solve a broad range of healthcare issues, everyday actors in healthcare are facing pressure to adopt AI where they can. At the same time, NATO’s security infrastructure is drawing from the same limited resource pool—particularly in terms of skills, energy, data infrastructure, and cybersecurity capacity. Without careful coordination, this could lead to a zero-sum scenario, undermining societal resilience and military advantage.

Cybersecurity threats to healthcare are well documented. The World Health Organization has recognized that cyber-attacks targeting health systems have considerable consequences in terms of public health and international security (WHO, 2024). In 2021, WHO reported that one-third of global healthcare institutions had suffered at least one ransomware attack in the preceding year (Mishra, 2024). The European Union reported that in 2023, healthcare was the most targeted critical sector in cyber-attacks (WHO, 2024). During the COVID-19 pandemic, healthcare was not just a target but a vector for disinformation and destabilisation by state and non-state actors alike. Given these risks, decisions about AI adoption in critical civil sectors like healthcare cannot be made in isolation from geopolitical and security considerations. Yet most local actors are not equipped to understand or navigate these complex dynamics. The absence of coherent guidance or frameworks linking AI adoption to national and international security exacerbates vulnerability, weakens societal resilience, and increases dependence on untrustworthy partners.

Global AI governance is essential. It can establish the guardrails necessary to manage these risks and guide responsible adoption of AI technologies across sectors. NATO has a critical role to play here. By integrating civil sector AI governance into its strategic thinking, and engaging with the Allies on this, NATO can help ensure that AI adoption enhances—not undermines—resilience and collective security. This will allow for a more realistic assessment of the trade-offs involved in AI adoption, especially in sectors like healthcare that are both vital to public well-being, are particularly vulnerable to attack and a conduit for hostile actors to cause societal disruption. NATO’s role here should be seen as complementing other international AI governance efforts, such as those by UNESCO, OECD and the EU etc. This would ensure that these governance structures do not become dominated by military priorities and bridge the gap between the defense and civil sector. Democratic safeguards, such as civil society oversight or public reporting, for any NATO-related initiatives affecting the civil sector, would also be essential. As would multidimensional and multidisciplinary views on civil resiliency frameworks.

AI in the civil sector is not a technical or administrative matter alone—it is a strategic issue with implications for the stability, security, and cohesion of NATO members’ and partners’ societies. Only through coordinated, international governance, can we navigate this new terrain with the prudence and foresight it demands.

References

Mishra, V., (2024) Cyberattacks on healthcare: A global threat that can’t be ignored, UN News, https://news.un.org/en/story/2024/11/1156751.

World Health Organization., (2024), Ransomware Attacks on Healthcare Sector ‘Pose a Direct and Systemic Risk to Global Public Health and Security’, Executive Tells Security Council, https://press.un.org/en/2024/sc15891.doc.htm.

Schaake, M., (2024). The Tech Coup. Princeton University Press.

Strange, M. and Tucker, J., 2024. Global governance and the normalization of artificial intelligence as ‘good’ for human health. AI & SOCIETY, 39(6), pp.2667-2676.

Further Information

This article is part of the Politics of AI & Health: From Snake Oil to Social Good funded by The Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS).

Keywords (comma separated):

NATO, Civil Sector, Artificial Intelligence, Security, Healthcare, Governance

Related URL (if any):

https://www.iffs.se/en/research/research-projects/politics-of-ai-health-from-snake-oil-to-social-good/

How to cite this article:

Tucker J. (2025). Civil Sector Vulnerabilities and NATO’s Strategic Role: The Case for International AI Governance. AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/UJML2174

Recording

AI Policy Lab Fellowship Information Session with Virginia Dignum

Date: May 6, 2025

Responsible AI Retreat at Lövånger 

On March 17-20, 2025 at Lövånger, AI Policy Lab (AIPL), Responsible AI group and Research Group for Socially Aware AI had a 3.5-day retreat with 15 participants from both units and Francien Dechesne from Leiden University, Netherlands.

Participants discussed responsible AI from multiple perspectives – technological, ethical, and social. Below we share the key messages from the retreat. We hope to inspire future multidisciplinary discussions, workshops and projects related to responsible AI research, literacy, and practical solutions for diverse stakeholders in Sweden and internationally.

Question Zero & Human Responsibility

When we hear about new AI ventures, we need to ask the question zero: “Is the adoption of an AI tool the best solution for our current problem?” with assessments including the environmental costs of running this AI system and understanding of what specific aspects of operations it improves. This includes critically assessing who is participating, what is the provenance and the management of data, what are the bases for modelling and design choices, how will results and impact be evaluated. AI does not happen to us, we [humans] design and/or adopt and use the AI systems. 

  • Responsible AI development should follow a compositional approach, where verified datasets and models with clear principles can be combined to create new systems. This framework emphasises the need to balance ethical considerations and accuracy while accounting for differences across sectors, industries, and global value systems. The approach prioritises sustainability, transparency, control, regulation, participation, inclusiveness, trust, and fairness, with a focus on measuring societal implications and ensuring equal representation and identification. 
  • True AI fairness goes beyond mathematical equality, considering historical disparities and contextual factors, and diverse definitions of fairness, as mathematical fairness alone can still create discriminatory outcomes in complex social settings. 
  • Participation, where people meaningfully contribute to (design) decisions, strengthens democracy and supports responsible design, development, and use of AI. However, participation alone does not guarantee better outcomes; thoughtful design is needed to prevent manipulation and address blind spots by incorporating diverse perspectives. 
  • Technological change is ecological, not additive, in that it is transformative and systemic rather than simply incremental, fundamentally reshaping existing environments and society. Drawing from Neil Postman’s (1995) framework, technological innovations fundamentally transform existing systems rather than simply adding new capabilities, creating trade-offs, winners and losers, and often leading to reframing how we perceive the world. 
  • Structural challenges. Several “traps” hinder responsible AI research: the framing trap (failure to model entire systems), portability trap (ignoring context sensitivity), formalism trap (oversimplifying social concepts), ripple effect trap (missing ecological impacts), and solutionism trap (overreliance on technological fixes). 
  • AI Art. The concept of “AI as a mirror” raises important questions about artistic expression, with significant differences between the inner experience of human artistic creation versus AI-assisted art generation, challenging traditional perspectives on IP rights, creativity (human only, genAI only, human-AI hybrid), intention, and artistic value. 

If the ideas above resonate with you, we encourage to check out the AI Policy Lab LinkedIn page for common cooperation opportunities. 

Beyond the AI race: why global governance is the greatest innovation

Virginia Dignum (Wallenberg Chair, Professor Responsible Artificial Intelligence, Director of AI Policy Lab at Umeå University)


Published on 8 April 2025
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As artificial intelligence (AI) evolves rapidly, the urgency to govern it responsibly becomes more pressing by the day. We are standing at a pivotal moment, one where the choices we make today will shape not just technological outcomes, but also the foundations of our societies, economies, and planetary well-being.

Even as the UN and other international agencies advocate for global AI regulation, major players – particularly the US, UK, and China – seem increasingly hesitant to fully commit. While the US and UK are currently moving towards light-touch, innovation-driven approaches that prioritize industry leadership over binding rules, China leans toward a state-controlled model aligned with its national priorities. Their reluctance undermines efforts to build the effective, inclusive governance frameworks we urgently need, and may encourage others to also sideline global cooperation in favour of fragmented, self-serving strategies.

But AI governance is not optional, it is essential. It protects rights, upholds global values, and ensures long-term economic stability and sustainable innovation. Without global governance, we open the door to a race to the bottom, marked by short-term thinking, ethical shortcuts, and growing global inequality. We cannot allow geopolitical competition to derail the collective responsibility required to ensure AI serves the common good. Now is the moment to strengthen our commitment to global, values-driven governance, not to stall it. Meanwhile, AI governance shouldn’t chase every new technology, but instead follow clear principles: transparency, fairness, explainability, and accountability. These form a foundation for adaptable policy that protects rights and safety. Tools like regulatory sandboxes, public engagement, and stronger international coordination support this flexible yet high-standard approach as AI evolves.

The best competitive advantage is not ruthless speed, but wise collaboration, especially when the stakes include trust, stability, and the health of our planet. In this context, the European Union’s €200 billion investment in regulated, human-centered AI stands out. This visionary approach demonstrates how regulation can act not as a brake on innovation but as a stepping stone for it. The EU’s commitment to ethics, inclusion, and sustainability offers a powerful alternative to the more narrowly competitive models pursued by the US and China. Yet, funding alone is not enough. Investment must be accompanied by sustainable practices, equitable access, and strengthened social cohesion. Other countries—Canada, Japan, Brazil—are also making important strides. But this is not a race with a single winner. It’s a collective effort, and meaningful progress depends on a globally aligned framework that ensures AI serves all of humanity.

Still, I am deeply concerned about the growing competition to dominate the AI landscape. China, the US, and others are increasingly viewing AI as a tool of economic and military supremacy. This race risks concentrating power in a handful of nations or corporations, sidelining most of the world and worsening inequality. China, the US, and others view AI through the lens of strategic dominance. But AI is not a zero-sum game. True progress requires transparency, ethical alignment, and shared governance.

One of the greatest ethical challenges today is the erosion of human agency through opaque, unaccountable AI systems. That’s why I advocate for Earth alignment, as we introduced in a recent article in Nature Sustainability. This framework emphasizes the need for AI governance to be anchored in environmental sustainability, global justice, and societal cohesion. These goals cannot be achieved in isolation or through regional silos. They require a shared commitment to values that transcend borders, and the democratization of governance. A small group of governments and companies cannot be allowed to shape society through their control over AI development, nor solely through the lens of existential threats and geopolitical rivalry.

Responsible development requires systemic change, not just technical fixes. Ethics must be embedded from the outset, but we must do so through systemic change, not fear-mongering. This is not just a question of innovation; it’s a matter of justice. AI should not be a tool that widens global divides or undermines democracy and social cohesion. It should be a force for empowerment, equity, and resilience. That’s only possible through shared governance, transparency, and ethical alignment across all borders, including those of the most powerful players.

This is why global governance of AI is not optional, it is urgent.

Looking to the future, what excites me most about AI is its potential to empower us, not to replace us. If we govern it well, AI could become one of our most powerful tools for addressing climate change, improving healthcare and education, and advancing equity and social cohesion. But that future is not guaranteed. It depends on the choices we make now. The future of AI is not just about building smarter machines and software, it is about working together towards a wiser humanity. One that values cooperation over competition, solidarity over supremacy. One that uses AI not to dominate, but to heal and uplift.

There is no alternative: in the long run, only responsible AI will lead to innovation that truly benefits society. Anything else will not only undermine trust and human rights but will also lead to technically weaker systems and a loss of true innovation. Irresponsible AI may promise short-term advantages, but it will cost us our long-term future.

Responsible AI is not the finish line. It is the only viable path forward.

Keywords (comma separated):

AI governance, global cooperation, responsible AI, sustainability, transparency, regulation

How to cite this article:

Virginia D. (2025). Beyond the AI race: why global governance is the greatest innovation. AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/LNQA3726

Potential impact of the EU Platform Work Directive on AI labelers

Mariia Lesina (Lund University)


Published on 25 March 2025
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Introduction

The gig economy has revolutionized labor markets, introducing high flexibility while simultaneously raising critical concerns about workers’ rights and protections. Across the European Union (EU), 3% of people aged 15-64 rely on digital platforms to access employment, yet many remain trapped in a legal grey zone, lacking social security, job stability, and transparency in how their labor is managed (Eurostat 2024).
While the attention has been largely drawn to the impact of AI algorithms on the workflows of such gig workers, most notably drivers or delivery people, one group has been largely overlooked – the invisible workforce behind AI. These workers perform essential tasks such as data tagging, annotation, and content moderation, which help train AI models (Muldoon et al. 2024, 9-10). Their work is often fragmented and distributed through digital labor platforms, making them susceptible to unfair algorithmic control, precarious employment conditions, and opaque remuneration structures. AI labelers work asynchronously across different time zones and are also subject to algorithmic management, where automated systems monitor their accuracy, determine their pay, and can even suspend or terminate their accounts without clear justification (Ibid, 12-15). Evidence of this can be easily found on community discussions on Reddit where stories of such workers are widespread, telling how their work was declined and payment delayed or cancelled altogether (Nagaraj Rao et al. 2025, 23). One of the recent attempts to address this systematic issue was taken by the EU via the Platform Work Directive, features and implications of which will be analyzed in this article.

EU Platform Work Directive

In 2024 the EU introduced the Platform Work Directive, a regulatory framework designed to enhance labor protections, redefine employment classifications, and establish oversight mechanisms for AI-driven algorithmic management. The EU Directive recognizes AI labeling as a form of platform work if it is conducted through a digital platform within the EU and based on a contractual relationship. Article 19 of Introduction specifically mentions tagging as a form of crowd work that can be conducted remotely. This recognition aims at allowing AI labelers to benefit from the Directive’s employment presumption when platforms exercise control over their workflows, task assignments, or performance evaluations. More importantly, it states that it is platforms that have to prove employment status of their platform staff, as the latter usually lacks the means and leverage to do so (Articles 30, 31).
The core of the regulations is that “platform work should be provided with rights, with a view to promoting transparency, fairness, human oversight, safety and accountability” (Article 14). These goals will be achieved through a number of legal changes, translated into the national legislation from the EU level. To begin with, Article 10 of Chapter III enforces human oversight in algorithmic decision-making and mandates greater transparency in how these systems operate, granting AI labelers the right to request detailed explanations of algorithmic decisions that impact their work. This means that platforms cannot solely rely on automated systems to suspend or deactivate an AI labeler’s account. Instead, these decisions must “ensure human oversight and regularly carry out an evaluation of the impact of individual decisions taken or supported by automated monitoring systems”: essentially demanding human in the loop, ensuring that workers are not unfairly penalized by flawed algorithms (Article 47). Additionally, AI labelers now have the right to contest algorithmic decisions, demand explanations, and request human reviews of automated rulings that affect their employment status, pay, or continued access to work (Article 8). The Directive also states that platform workers’ representatives “should be involved in the evaluation process” of these automated systems (Article 44).
The mental and physical well-being of platform workers is another crucial aspect addressed by a separate Article 12 of Chapter III. Due to the nature of their work – repetitive tasks, exposure to harmful content, and tight deadlines – AI labelers face unique risks to their mental and physical health. The Directive requires platforms to assess and mitigate these risks, ensuring that AI systems do not “put undue pressure on platform workers or otherwise puts at risk their safety and physical and mental health”. Platforms must now provide effective information and consultation for workers, while Member States – ensure that digital labour platforms take “preventive measures, including providing for effective reporting channels” (Article 12 of Chapter III).

Possible shortcomings of the Directive

While the Directive provides a theoretically effective legal framework, its success is based on effective enforcement. One major concern is that companies will exploit national law loopholes, restructuring their business models to avoid classifying workers as employees. This issue has been explored by an organization called Fairwork, which evaluates the work conditions of digital labour platforms, and whose extensive analysis highlights large reliance on contextual enforcement in the country-specific legislation. Therefore, as Fairwork experts state, in member states where the “power of labour unions is undermined”, like, for instance, in Italy, the workers can remain unprotected and “self-employed” (Fairwork Project 2024, 2).
Moreover, as experts from International Labour Organization (ILO) explore, outsourcing work to foreign workers in countries where the cost of labour is lower is common as it enables businesses to optimize their costs (Rani et al. 2021, 22). Hence, the Directive, which only protects workers within the EU, does not address the issue of exploitation of non-EU based platform staff.

Conclusion and further discussion

The EU Platform Work Directive is a crucial step toward recognizing AI labelers as platform workers, granting them employment protection, transparency, and human oversight in algorithmic management. However, its impact depends on consistent enforcement across Member States and preventing platforms from exploiting loopholes or outsourcing labor beyond the EU’s reach.
As the December 2026 deadline for national implementation approaches, the fight for fair AI labor practices is only just beginning. With platforms already pushing back, arguing that regulation stifles innovation and contradicts the inherent advantage of flexibility that digital work provides (Copenhagen Economics 2021, 24), the real test will be in Directive’s practical integration. Whether the EU emerges as a pioneer in ethical AI labor governance or struggles with unintended consequences will depend on the ability of each state to implement the Directive in a way that is both principled and pragmatic. Hence, the most topical question remains: “Will this policy set a global precedent for fair AI labor practices, or will fragmented enforcement and corporate resistance weaken its impact?”
To achieve the first outcome, EU policymakers, researchers, and labor rights advocates will need to continue pushing for a regulatory environment that ensures that this overlooked groups in the gig economy – AI labelers – receive the protections they deserve.

References
1. Copenhagen Economics. The value of Flexible work for local delivery couriers. Study for Delivery Platforms Europe. November 2021. 28 p.
2. European Parliament and Council Directive (EU) 2024/2831 of 23 October 2024 on improving working conditions in platform work [2024] OJ L283/1. Internet source. URL: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32024L2831 (accessed 7 March 2025).
3. Eurostat. Experimental statistics on digital platform employment. 18 July 2024. Internet source.URL: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20240718-1#:~:text=In20223.0ofpeople,countriesand1EFTAcountry (accessed 7 March 2025).
4. Fairwork Project. Fairwork’s Response to the EU Directive on Platform Work. March 2024. Internet source. URL: https://fair.work//srv/www/wp-content/test.aipolicylab.se/uploads/sites/17/2024/03/Fairworks-Response-to-the-EU-Directive-on-Platform-Work.pdf (accessed 7 March 2025).
5. Muldoon, J., Graham, M., Cant, C. Feeding the Machine: The Hidden Human Labour Powering AI. Canongate Books. 2024. 288 p.
6. Nagaraj Rao, V. Dalal, S., Agarwal, E., Calacci, D., and Monroy-Hernández, A. Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy. ACM Hum.Comput. Interact. No. 9, 2. April 2025. Pp. 1-49.
7. Rani, U., Rishabh, K.D., Furrer, M. G?bel, N. Moraiti, A. and Cooney, S. World employment and social outlook: the role of digital labour platforms in transforming the world of work. Geneva: International Labour Office. 2021. 283 p.
8. Silberman, M.S., Adams-Prassl, J., Abraha, H. and Suresh, R., Doth the Platform Protest Too Much? Uber, Employment Status, and the EU’s Proposed Platform Work Directive. Oxford Law Blogs, 28 September 2023. Internet source. URL: https://blogs.law.ox.ac.uk/oblb/blog-post/2023/09/doth-platform-protest-too-much-uber-employment-status-and-eus-proposed (accessed 7 March 2025).

Keywords:
EU Platform Work Directive, AI labeling, Gig Economy, Human Rights

How to cite this article:

Lesina M. (2025). Potential impact of the EU Platform Work Directive on AI labelers. AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/APJD5342

Metadata/README elements for synthetic structured data made with GenAI: Recommendations to data repositories to encourage transparent, reproducible, and responsible data sharing

Ericka Johnson (Dept of Thematic Studies, Linköping University)
David Rayner (Swedish National Data Service, University of Gothenburg)
Jools Kasmire (University of Manchester), Victor Hennetier (Dept of Thematic Studies, Linköping University), Saghi Hajisharif (Dept of Science & Technology, Linköping University), Helene Ström (Fair AI Data)


Published on 20 March 2025
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Introduction

Publication of AI-generated synthetic structural data in data repositories is beginning to reveal the specific documentation elements that need to accompany synthetic datasets so as to ensure reproducibility and enable data reuse.
 
This document identifies actions that research repositories can take to encourage users to provide AI-generated synthetic datasets with appropriate structure and documentation. The recommendations are specifically for AI generated data, not (for example) data produced using pre-configured models or missing data created by statistical inference. Additionally, this document discusses metadata/README elements for synthetic structured datasets (tabular and multi-modal) and not textual data from LLMs or images for computer vision. 

The document is the result of a workshop held on 23rd January 2025, with participants from the Swedish National Data Service, Linköping University and Manchester University. It also draws on survey responses about current practice from 17 data repositories and a review of existing metadata and README requirements. 

Background

AI-generated synthetic structured datasets are generated using machine learning techniques with the aim of reproducing the essential elements of an existing dataset (Guépin et al., 2024 Jacobsen, 2023; Li et al., 2023; Offenhuber, 2024; Savage 2023). Synthetic data generation may be driven by the need to ensure privacy or to expand, enhance or substitute for real-world datasets which may be insufficient or non-existent. Sometimes synthetic data is produced to create a portable or shareable dataset that is considered safe for open access, for example to share via a data repository. 

While synthetic structured data may reproduce the essential elements of an original dataset, it will also inevitably introduce “intersectional hallucinations”, which refer to anomalous inter-attribute relations within a dataset (Lee, Hajisharif & Johnson 2025). AI generated synthetic data also have a known tendency to minimize minority elements and amplify majority elements (Chen et al., 2024; Johnson & Hajisharif 2024). Thus, knowing in what ways a synthetic dataset demonstrates fidelities and in what ways it is ‘different’ from the original data is essential for successful and responsible re-use of synthetic data. Given that the goal of many data repositories is to provide access to data that is replicable and/or reusable, there is a clear need to establish protocols for documenting synthetic data.

Primary recommendations

Our primary recommendations are:

a) that data repositories establish a standardized way to label data as synthetic data, and that this information is prompted-for or required in the metadata or READMEs associated with synthetic datasets. 

b) that data repositories provide users with a guide that explains how to properly document synthetic data. The extent to which documentation should be provided with the dataset or provided in associated articles or publications linked to the data will depend on the policies of the repository. An example is the guide provided by the Swedish National Data Service (2025). 

c) that domain experts be prompted to document the context and motivation for generating synthetic data.

Documenting synthetic data – process and product

Reusability often refers to the data as a product. In the case of synthetic structured data, however, it may be the method of data generation (the data as a process) that  is reusable, not the data itself. We therefore suggest that data repositories require information about both the process of data generation and details about the actual synthetic data.

Data as a process

The following elements should be included to describe the technical details of the synthetic structured data generation process: 

  • A description of the workflow.
  • The generative model used (i.e. GAN, Diffusion, etc.). As techniques are constantly evolving, these requirements should be formulated in such a way to allow for and capture new techniques. The structure and hyperparameters (learning rate, number of epochs, etc.) of the generative model are also important factors for reproducibility and should be included.
  • What raw data or inputs, if any, were used, including its mode of collection. A link to the source of the raw data should be provided where appropriate.
  • Which (random) seeds were used.
  • If a subset of raw data were reserved for testing, how was this subset selected?
  • Versions of the software and packages used.
  • Operating system information, values for relevant environment variables.
  • A link to the source code (we suggest keeping code in a separate repository so it can be reviewed, improved, and re-released) and if appropriate a link to the weights of the trained model.
  • Citation details (including DOIs) for related documents or the release versions of code.

Additionally, some cases of synthetic data are not based on raw data (e.g. agent based modeling/multi agent systems, digital twins). In such cases, this should also be clearly stated in the description of the data generation process. 

If a repository considers that publishing the data generation model is out-of-scope, we suggest providing information on how models can be deposited in either a more generic repository or in a specific repository for models. Links to the model can then be provided in the dataset metadata and/or README.

Product

Synthetic structured datasets inevitably contain stochastic variability, meaning that different datasets can be obtained by running the same code multiple times with different random seeds. We therefore suggest that metadata/READMEs also contain information about:

  • whether the dataset is entirely synthetic or augmented. If it is augmented, what are the proportions of real and synthetic data?
  • missing edge cases at the single-attribute level and inter-attribute level. 
  • inter-attribute combinations in the raw data that have diminished frequency in the synthetic data.
  • inter-attribute hallucinations that have been observed in the synthetic data. 
  • details of the verification/validation process: how was the model tested, etc.
  • how the synthetic data are structured at the file-level: are the input data in a folder marked “raw” or “input”, and output in an “output” folder?

Privacy and specific circumstances

A common use-case for synthetic data is when privacy assurance is necessary for sensitive data. In such cases, we recommend the metadata/README contain information about disclosure risk, indication risk, reidentification risks, K-anonymity, etc. This type of synthetic data requires extra care and should only be made freely available if specific individuals cannot be re-identified by any reasonably likely means.

We also suggest that repositories include instructions on creating the metadata/README that will prompt domain experts to explain the specific circumstances of their synthetic data. Why was it generated? What is the fundamental hypothesis behind the synthetic dataset’s use? What is its subject and purpose(s)? Data creators should be encouraged to disclose, for example, if the dataset was created for exploratory research, to represent sensitive data, to allow for work by a distributed team, to enable data portability, to create categories or support classification decisions, etc.. Encourage data submitters to consider sensitive areas and intersections within the data, as well as how many relational intersections are valuable to combine when using the dataset for new research purposes.

Summary and discussion

Synthetic structured data may be produced where scientific research requires data with no personal information, data that are portable and shareable, data which are not obtainable for practical or ethical reasons, or large datasets for machine learning. However, the details of the generation process and the variations inherent in synthetic data need to be documented, either in a dataset’s metadata/README or in the articles accompanying the dataset.

Many aspects of synthetic data are still emerging, and in some cases, we lack established routines or even vocabularies for them. We hope the recommendations in this policy document will serve as a starting point for further discussions. In particular, we aim to encourage those working with data repositories to collectively establish best practices for managing synthetic data and developing vocabularies to describe them. For example, we might promote an accepted keyword or subheading, such as SYNTHETIC_DATA, or suggest appending “_synth” to filenames containing synthetic data. Additionally, controlled vocabularies should include subcategories to distinguish between fully synthetic and blended/augmented data.

With a well-defined vocabulary and clear metadata guidelines, repositories can help researchers to describe both their datasets and the processes used to create them in an open, transparent, and reproducible manner, ensuring responsible data sharing within the scientific community.

References

??Chen W, Yang K, Yu Z, et al. (2024) A survey on imbalanced learning: latest research, applications and future directions. Artificial Intelligence Review 57(6): 137.

Guépin F, et al. (2024) Synthetic Is All You Need: Removing the Auxiliary Data Assumption for Membership Inference Attacks Against Synthetic Data. In: Katsikas, S., et al. Computer Security. ESORICS 2023 International Workshops. ESORICS 2023. Lecture Notes in Computer Science, vol 14398. Springer, Cham. https://doi.org/10.1007/978-3-031-54204-6_10.

Jacobsen BN (2023). Machine learning and the politics of synthetic data. Big Data & Society. 10(1).  

Johnson E and Hajisharif S (2024) The intersectional hallucinations of synthetic data. AI & Society. https://doi.org/10.1007/s00146-024-02017-8. 

Lee, Hajisharif & Johnson (2025) The ontological politics of synthetic data: normalities, outliers, and intersectional hallucinations. Big Data & Society.

Li X, Wang K, Gu X, Deng F, Wang FY (2023) Parallel eye pipeline: An effective method to synthesize images for improving the visual intelligence of intelligent vehicles. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 53(9), 5545-5556.

Offenhuber D (2024) Shapes and Frictions of Synthetic Data. Big Data & Society. 11 (2): 20539517241249390. https://doi.org/10.1177/20539517241249390.

Savage, N (2023) Synthetic data could be better than real data. Nature Machine Intelligence. doi: https://doi.org/10.1038/d41586-023-01445-8.

Swedish National Data Service. (2025). Managing and publishing synthetic research data (Version 1). Zenodo. https://doi.org/10.5281/zenodo.14887525 

How to cite this article:

Johnson, E., Rayner, D., Kasmire, J., Hennetier, V., Hajisharif, S., & Ström, H. (2025). Metadata/README elements for synthetic structured data made with GenAI: Recommendations to data repositories to encourage transparent, reproducible, and responsible data sharing. AI Policy Exchange Forum (AIPEX). https://doi.org/10.63439/MPEW5336