Dear colleagues, We are pleased to announce the call for papers for the upcoming issue of the *ATIC Journal *dedicated to the theme: *Artificial Intelligence, Commons, and Collaboration*. *Guest Editors:* - *Béa Arruabarrena*, CNAM – DICEN Laboratory - *Stéphan-Eloïse Gras*, CNAM – DICEN Laboratory *Important dates:* - *Deadline for abstract submission:* October 30, 2025 - *Notification of acceptance:* November 30, 2025 - *Full paper submission (30,000–50,000 characters):* April 1, 2026 - *Publication:* Summer 2026 Although the journal publishes in *French*, we welcome *submissions in English*. Further details, including the full call for papers and submission guidelines, are available here (in French): https://www.dicen-idf.org/intelligence-artificielle-communs-collaboratif/ We warmly invite contributions that engage critically with the intersections of artificial intelligence, commons, and collaborative practices. Sincerely, *Stéphan-Eloïse Gras* and *Béa Arruabarrena* Since the public release of ChatGPT in November 2022, the media sphere has witnessed intense debates and passionate interventions from experts and scholars across diverse fields—computer science, linguistics, biology, statistics, ethics, law, among others. Each field feels directly implicated in the spread of so-called “generative” artificial intelligence technologies, which rely on connectionist deep learning techniques. The proliferation of these artefacts, commonly referred to as AI (by metonymy of the sub-discipline they originate from), tends to obscure the material and sociotechnical conditions of their production. Indeed, AI artefacts emerge from an often-overlooked assemblage of multiple computer engineering traditions: systems and network computing, robotics, software engineering, expert systems or symbolic AI, machine learning, and deep learning. The aim of this issue is precisely to analyze the composite and heterogeneous nature of AI artefacts, to take into account the social and organizational dynamics underlying their existence, and to question the very existence and regimes of AI “commons” (i.e., open datasets, models, or weights). Reducing AI to generic objects (as in the case of “general AI”; Julia, 2019), intelligent artefacts (Agostinelli & Riccio, 2023), or nominalist abstractions (Bachimont, 2014) conceals their composite character as well as the collective dynamics and power relations that bring them into being. Conversely, a sociotechnical perspective (Flichy, 2008) reveals that the making of AI artefacts rests on at least three simultaneous processes: (1) the elaboration of a global application perspective; (2) data collection, classification, and processing; and (3) practices and uses. Focusing on “frames of use” draws attention to alignment phases, where artefacts are adjusted for social acceptability or a “license to operate” (Alcantara & Charest, 2023). The sociotechnical approach thus highlights the inseparable social and technical, material and discursive dimensions of information and communication systems. This issue of ATIC calls for critical inquiry into the collective organizations—firms, research laboratories, public or nonprofit bodies, independent initiatives—responsible for the design and development of AI artefacts. In line with Taylor (2011) and from a pragmatic standpoint, attention is directed to the constitution of organizational forms that “make” AI: material operations, products, websites, statements, speech acts, and discursive practices enacted within situated contexts (Cooren, Brummans & Charrieras, 2008; Cooren, 2024). Such an approach also requires investigating the asymmetries resulting from the centralization of design and development in the hands of a few dominant actors, and their consequences for information and communication practices (Ertzscheid, 2023). The fragile “bigger is better” narrative has subjected AI to a purported “law of scale” (Varoquaux et al., 2024), in which the usefulness and performance of systems are conditioned by the sheer size of algorithmic architectures, characterized by trillions of parameters. Similarly, the design, training, and deployment of AI models—today largely dominated by a small number of private actors—limit possibilities for collective appropriation. This hyper-concentration directly conflicts with the logic of digital commons, which rests on openness, sharing, distributed governance, and the empowerment of user communities. AI as currently organized tends to consolidate regimes of exclusive property and unilateral surveillance (Zuboff, 2019), rather than foster co-production and the circulation of knowledge and technological tools. Accordingly, this issue of ATIC invites analysis of AI commons and their sharing regimes: negotiation spaces where AI must serve collective projects (Pene, 2017). Contributions are encouraged to address collaborative dynamics, including emerging forms of collaboration and participatory methodologies enabled by transdisciplinary approaches. Digital commons are defined as collectively produced and maintained digital resources, governed by rules that preserve their shared and collective character (Baudoin, 2023). These involve researchers, citizens, and public institutions confronting the complex challenges of AI commons (Fitzpatrick, 2019). Such an approach views AI artefacts as human productions dependent on collective resources and methodologies, serving a more collaborative economy (Benkler, 2011) grounded in open ecosystems (Bauwens, 2005). Collective, diverse frameworks have demonstrated their capacity to produce and regulate technologies as commons through shared governance. While research has documented the role of major corporations in the development of open-source projects such as Linux (Broca, 2013), the centrality and ambiguity of open-source artefacts (datasets, models, algorithmic architectures, weights, etc.) in contemporary generative AI calls for renewed attention. Engaging the notion of commons also foregrounds the ethical stakes of AI. Against utilitarian and accelerationist perspectives, which dominate discourses emphasizing efficiency and profitability (Bostrom, 2014; Tegmark, 2017) often at the expense of distributive justice and the most vulnerable (Rawls, 1987), a commons-based ethic, following Haraway (1988), Star (1999), Zacklad & Rouvroy (2022), highlights power relations and social asymmetries. It expands debates toward equity, social justice, and inclusion. Noble (2018) demonstrates how algorithmic biases reflect historical inequalities embedded in data and design choices, while participatory and open research approaches help mitigate systemic biases (Fitzpatrick, 2019). Open-source collectives thus provide concrete examples to examine both the potentials and the limits of this approach. In a similar vein, Floridi (2013) advocates deliberative processes where citizens, developers, regulators, and users co-develop ethical norms responsive to contemporary challenges. Finally, this issue of ATIC, by interrogating AI sociotechnics through the lens of collaboration and commons, invites reconsideration of the status of knowledge, content, and interactions produced by AI. Under what conditions can AI artefacts—language models, generative applications, datasets—be conceived as spaces of creation, collective intelligence, and experimentation, grounded in co-construction, mutualization, and regulation? We invite contributions from scholars across disciplines—information and communication sciences, sociology, anthropology, law, computer science, design—as well as from digital commons practitioners and communities. Submissions may be theoretical or empirical, provided they engage with the ways commons and collaboration reshape the AI landscape. *Suggested Themes* *Axis 1 – Sociotechnical approaches to AI artefacts* Exploring AI as sociotechnical systems by analyzing artefacts, infrastructures, and conditions of implementation. Contributions may address materiality, technical inscriptions, and collective arrangements that render AI operative, as well as issues of access inequalities, invisibilized labor, translation processes, and technological lock-in. Topics may include AI design processes, data corpus construction, language model use, open/expert agent interactions, or shared infrastructures such as Hugging Face, GitHub, and other platforms. Special attention may be given to opacity, standardization, and modularity that support large-scale collaboration, and to tensions between community innovation and dependence on dominant models. *Axis 2 – Collaborative and organizational dynamics of AI* Analyzing AI as organizational, collective, and experimental practice. This includes forms of coordination, cooperation, and governance in the design, development, and use of AI. Topics may cover organizational practices, skills, literacies, participatory AI methodologies, collaborative robotics, or emergent governance of digital commons, platforms, and data. Empirical studies documenting usages, experimental contexts, or organizational innovations are particularly welcome. *Axis 3 – Epistemological, ethical, and political issues* Critical and reflexive perspectives on AI’s knowledge regimes, ethics, and politics. Contributions may focus on discourses and practices around governance and digital sovereignty, the knowledge and norms structuring AI artefacts, or the asymmetries of power arising from data economies, computing infrastructures, or regulation. Submissions may explore resistance, counter-expertise, or citizen mobilizations (e.g., “Slow AI”, Data Detox), situated ethics, or the geopolitics and environmental costs of AI infrastructures. This axis also invites reflection on the conditions for commons-based AI that reshape relations between technology, power, and society. *Submission Guidelines* Submissions should include: - Author identity, institutional affiliation, and title on the first page; anonymized title and text on subsequent pages (doc/odt format). - A clear and explicit title. - An abstract (max. 3,000 characters, excluding references) outlining the research problem, theoretical framework, methodology, and expected results or contributions. - A list of references. *Timeline* - *Submission of proposals (max. 3,000 characters):* October 30, 2025 - *Notification of acceptance:* November 30, 2025 - *Full paper submission (30,000–50,000 characters):* April 1, 2026 - *Publication:* Summer 2026 Although the journal publishes in *French*, *submissions in English are welcome*. *Contact:* beatrice.arruabarrena@lecnam.net stephan-eloise.gras@lecnam.net revue@revue-atic.fr