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CALL FOR PAPERS & ARTWORKS
(Music, sound art, visual art, performance, installation)

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The contemporary emergence of generative artificial intelligence systems is profoundly transforming the conditions for the production, perception, and conceptualization of symbolic forms. As demonstrated by Galloway (2012), Hansen (2015), and Hayles (2017), computational systems must no longer be understood as mere instruments external to human activity, but rather as operative entities that actively participate in the constitution of the sensible field itself. Indeed, since the emergence of technological arts, artists have been experimenting with instruments that impose their own data and parameters, leading to a primary co-agency and the complexification of the question of apparatuses (Foucault, 1961; Duguet, 1980; Brandon, 2021). The ongoing mutation since 2022, with the launch of ChatGPT by OpenAI, dictates a radical renewal of the critical question: the aim is not to analyze AI from an external position (which is impossible to maintain), but to develop an intrinsic, experimental, and fully co-agentive critique capable of operating from within the computational processes themselves.

The critique of artificial intelligence cannot, in fact, constitute itself as a meta-position. It demands co-agency, understood as an operative relationship in which the artist and/or the scholar engage directly within the computational regimes, architectures, and data flows that produce forms (Mackenzie, 2017). This necessity has been apparent since the earliest analyses of connectionist systems, notably through the exposure of structural biases embedded in datasets and architectures (Buolamwini & Gebru, 2018; Crawford, 2021). Co-agency thus constitutes the condition for accessing the effective materiality of AI systems, which is irreducible to their interfaces or perceptible effects, residing instead in their generative processes, vectorial dynamics, and optimization regimes. These systems no longer merely constitute technical objects, but operative milieus within which humans and computational processes co-produce symbolic configurations and mutually challenge one another, leading to the constitution of new mechanisms of alterity (Le Coarer et al., 2026).

The task, then, is to continue unfolding our relationship to the alterity proposed by Western anthropology. Tim Ingold (2017, p. 25) defines this field of knowledge as "philosophy with the people in"; it consists of "learning with and learning from"; it opens up a life process that engages a transformation of the process itself. For François Laplantine (1987, p. 64), "what the researcher experiences in their relationship with their interlocutors (what they repress or sublimate, what they detest or cherish) is an integral part of their research." Thus, for Nicolas Bourriaud (2021, p. 186), the major question common to both anthropologists and artists is the adjustment of this precise distance taken with the interlocutor (and with reality as a whole), which enables the production of knowledge rather than the reproduction of the already-known.

In the current context, the objective is therefore to question and interact with the ontology of artificial intelligence systems, which cannot be conceived in terms of objects, but must be understood as a relational, vectorial, and distributional ontology. The production of images, sounds, or texts by AI systems does not correspond, therefore, to a simple reproduction or recombination of existing data, but rather to the emergence of novel configurations made possible by the vectorial structuring of the model's internal relationships. These processes engage specific forms of alterity that pertain neither to human alterity nor to technical alterity in the classical sense, but to a computational alterity resulting from statistical, architectural, and optimizational dynamics that are irreducible to human intentionality (Boisnard, 2026). Even if it means exploring, as of now, the future critical moment when AIs self-code and self-generate, precipitating the "autopoietic take-off" (Teubner, 1993) of computational alterities; thereby reconfiguring social class relations in the process (Lordon, 2026).

A shift in the epistemic approach is consequently implied. Following the approaches developed in feminist and queer epistemologies, in critical phenomenologies of social structures (Ahmed, 2006), in new materialisms (Barad, 2023), or in analyses of horror as a destabilization of perceptual structures (Trigg, 2017), it becomes necessary to recognize the situated, engaged, and material nature of experience. Critique no longer proceeds through distancing, but through immersion and co-participation in the studied processes with full "response-ability"—following Donna Haraway’s (2016) reformulation—that is, an accountability of action that connects human and non-human entities.

Contemporary artistic practices play a central role in the exploration of these regimes of co-agency. Numerous works in research and art interrogate what emerges from generative AIs, such as those by Jake Elwes (2021), which unveil the normative structures embedded in datasets and thereby question the queer phenomenon. Generative sound experiments, for their part, explore the temporal and perceptual regimes specific to computational systems, while the analytical approaches developed in the field of cultural analytics (Manovich, 2020) serve to highlight the statistical and vectorial structures that organize contemporary cultural forms.

These artistic and theoretical practices thus contribute to the emergence of a new form of critique, founded no longer on the interpretation of works as intentional expressions, but on the analysis of the operative regimes that make their production possible. Critique becomes an experimental practice where, as Boutet de Monvel (2023) expresses it, the goal is to inject noise, an alterity, in the sense that the artist "strives to magnify the internal and/or external noise of a given medium, contrary to engineering, which seeks to suppress it."

Consequently, this symposium proposes to explore the various dimensions of this co-agency between humans and artificial intelligence systems, examining the ways in which it relies on forms of alterity and alteration that reveal the distinct powers of computational processes, by bringing together artists and scholars from diverse disciplinary fields.

Topics


References

Ahmed, S. (2006) Queer Phenomenology: Orientations, Objects, Others, Duke University Press
Barad, K. (2023) Frankenstein, la grenouille et l’electron. Les sciences et la performativité queer de la nature, Asinami.
Boisnard P. (2026), The Algorithmic Unconscious: Structural Mechanisms and Implicit Biases in Large Language Models arXiv:2602.18468 [cs.CY]
Boutet de Monvel V. (2023) , « Cybernetic subjectivities on a loop : From video feedback to generative AI », in Necsus European Journal of Media Studies. vol. 12, no. 2.
Buolamwini, J.,Gebru, T. (2018) "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification » Proceedings of Machine Learning Research.
Crawford, K. (2021), Atlas of AI. Yale University Press, 2021.
Haraway, D. Staying with the trouble, Duke University
Galloway, Alexander R. (2012) The Interface Effect. Polity Press.
Hansen, Feed-Forward, 2015
Hayles, Unthought, 2017
Manovich, Cultural Analytics, 2020
Mackenzie, A. (2017). Machine Learners: Archaeology of a Data Practice. MIT Press.
Teubner, G. (1993). Le Droit, un système autopoïétique, PUF,
Trigg, D. (2017). The Thing - Une phénoménologie de l'horreur, Editions MF

 

 







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