public data lab

Call for Abstracts: "Assembling Critical AI Studies: Concepts, methods, interventions", edited volume with meson press

by Public Data Lab

Cross-posted from the Deep Culture project blog.

In recent years, artificial intelligence (AI) technologies have been subject to intense hype (Campolo & Crawford, 2020), with the rise of Transformer-based LLMs leading to the widespread societal adoption of AI systems. As a response to these developments, critical scholars across scientific disciplines have quickly noted the new logics instantiated by way of these AI models, whether political (e.g., Amoore et al., 2024), cultural (e.g., Weatherby, 2025), economic (e.g., Luitse & Denkena, 2021), or epistemic (e.g., Impett & Offert, 2026). These studies have crucially underlined the need for critical perspectives from the social sciences and humanities, opening up otherwise opaque and foreclosed systems for enquiry and critique.

As these systems continue to become even more pervasive, critical AI research remains vital to understand and enable public debate on the role of AI in society. While the last few years have seen ongoing formation of Critical AI Studies (see e.g., Raley & Rhee, 2023; Offert & Dhaliwal, 2025), questions around what AI is and how to study it critically remain open and contested. As part of this emerging field, scholars have sought inspiration in various disciplinary approaches, including literature, history, science and technology studies (STS), media studies, security studies, and geography. These efforts have produced a range of work, including STS-inspired ethnographic studies of ground truths (e.g., Jaton, 2021) and AI evaluation practices (e.g., Schjøtt & Blanke, 2026), along with analyses of AI materialities (e.g., Rella, 2024) and architectures, such as the Transformer  (e.g., Dhaliwal et al., 2024). Yet the field still faces challenges in problematising the “thingness” of AI (Suchman, 2023), prompting wider debates about how to critically study AI as an empirical object (Lee & Ribes, 2026). What, in other words, might it take for Critical AI Studies not to run out of methodological and conceptual steam (Latour, 2004)?

In order to both contribute to societal debates on the role of AI and push the boundaries of Critical AI Studies, we invite contributions to a new edited volume on “Assembling Critical AI Studies: Concepts, methods, interventions”. The volume seeks to advance Critical AI Studies by thinking from within the vernaculars, materialities, and epistemologies of AI by means of critically situating concepts, advancing innovative methods, and acts of intervention in the public life of AI. 

We specifically invite chapters of 4,000-6,000 words that expand entry points to the critical study of AI across three thematic sections of the volume that respectively:

  • Theoretically engage with relevant, underexplored emic concepts (e.g., pipeline etc.);
  • Develop innovative methods for the critical study of AI; or
  • Pursue creative/artistic interventions that unsettle sedimented understandings of AI.

The edited volume will be published with meson press, which already features several valuable critical works on AI, such as Algorithmic Authenticity, Neural Networks, and Vector Media. The editorial team behind the volume consists of Anna Schjøtt, Dieuwertje Luitse, Louis Ravn, Nanna Thylstrup, Rocco Bellanova, and Tobias Blanke.

Submission details & timeline

We invite abstracts of 500 words (excl. references) to be submitted to AssemblingCAIS@proton.me by November 30. Please indicate what section of the book (concepts, methods, or interventions) you wish to be included in. All accepted authors are encouraged to participate in a two-day physical workshop organised at the University of Amsterdam from April 1-2, 2027. At the workshop we will collaboratively engage with the chapters as works-in-progress and identify overarching connections across the edited volume. The deadline to submit the draft chapters is March 15 to allow other authors to prepare feedback for the workshop. The final chapters are to be submitted June 15 and will then undergo peer-review. The aim of publication is early 2028. 

References

Amoore, L., Campolo, A., Jacobsen, B., & Rella, L. (2024). A world model: On the political logics of generative AI. Political Geography, 113, 103134. https://doi.org/10.1016/j.polgeo.2024.103134 

Campolo, A., & Crawford, K. (2020). Enchanted Determinism: Power without Responsibility in Artificial Intelligence. Engaging Science, Technology, and Society, 6, 1–19. https://doi.org/10.17351/ests2020.277 

Impett, L., & Offert, F. (2026). Vector Media. University of Minnesota Press. 

Jaton, F. (2021). The Constitution of Algorithms: Ground-Truthing, Programming, Formulating. The MIT Press. https://doi.org/10.7551/mitpress/12517.001.0001 

Lee, F., & Ribes, D. (2026). Computational Universalism, or, Attending to Relationalities at Scale. Social Studies of Science, 56(3), 325–339. https://doi.org/10.1177/03063127251345089 

Luitse, D., & Denkena, W. (2021). The great Transformer: Examining the role of large language models in the political economy of AI. Big Data & Society, 8(2), 1–14. https://doi.org/10.1177/20539517211047734 

Offert, F., & Dhaliwal, R. S. (2025). The Method of Critical AI Studies, A Propaedeutic (arXiv:2411.18833). arXiv. https://doi.org/10.48550/arXiv.2411.18833 

Raley, R., & Rhee, J. (2023). Critical AI: A Field in Formation. American Literature, 95(2), 185–204. https://doi.org/10.1215/00029831-10575021 

Rella, L. (2024). Close to the metal: Towards a material political economy of the epistemology of computation. Social Studies of Science, 54(1), 3–29. https://doi.org/10.1177/03063127231185095 

Schjøtt, A., & Blanke, T. (2026). Making machine learning good enough – studying the political endeavour of finding ‘right’ metrics and thresholds. Big Data & Society, 13(2). https://doi.org/10.1177/20539517261429198

Singh Dhaliwal, R., Lepage-Richer, T., & Suchman, L. (2024). Neural networks. University of Minnesota meson press.

Suchman, L. (2023). The uncontroversial ‘thingness’ of AI. Big Data & Society, 10(2), 20539517231206794. https://doi.org/10.1177/20539517231206794 

Weatherby, L. (2025). Language Machines: Cultural AI and the End of Remainder Humanism. University of Minnesota Press.