Dear colleagues,
we invite contributions for a special issue in Technological Forecasting and Social Change on "AI for whom and by whom? Cultural bias and the institutional and social shaping of large language models".
The special issue examines large language models (LLMs) as socio-technical systems shaped by governance regimes, public research organizations, and artificial intelligence (AI) ecosystems and invites empirical research contributions on cultural bias, digital colonialism, comparative regional ecosystems, and universities' roles in LLM development. It asks explicitly who is developing LLMs, for what purposes, in which organizational, institutional, and territorial contexts. It invites analyses that treat LLMs not only as technical artifacts, but as outcomes of specific institutional settings, innovation policies, business models, and knowledge cultures, embedded in concrete ecosystems and networks.
The Special Issue welcomes empirical papers drawing on all type of methods. Contributions need to engage with LLMs and their institutional and cultural implications, and should aim to shed light on one or more of the following, or closely related, themes:
• How LLM training data, model design, and deployment practices are embedded in specific cultural, linguistic, institutional, and regional contexts, and how this shapes what is represented or silenced.
• Cultural bias, digital colonialism, knowledge extractivism, and the treatment of local languages, indigenous knowledge, and alternative epistemologies in LLM development and use.
• The structure and evolution of LLM related ecosystems and value chains, including the roles and power of cloud providers, platform firms, data holders, start-ups, and sector specific complementors.
• Comparative analyses of national or regional LLM ecosystems, examining differences in industry architecture, public policy, data governance, and strategic positioning across countries or blocs such as the United States, the European Union, China, and regions of the Global South.
• The contributions and strategies of public research organizations, universities, and university networks in LLM research and deployment, including university industry collaborations, national initiatives, and across border alliances formed to pool scarce compute, data, and expertise.
• Case studies exploring how participatory foresight, co-design, or public deliberation can shape LLM development and deployment toward more socially accountable, sustainable and future-oriented outcomes.
Technological Forecasting and Social Change is a top interdisciplinary journal that combines insights from science and technology studies, innovation research and policy, and the social sciences at large. It has a very high Journal Impact Factor and is ranked as a "3" in the ABS Academic Journal Guide and as an "A" in the ABDC Journal Quality List.
The Special Issue will be open for submissions on September 1, 2026. Please submit your manuscript to the journal's Editorial Manager® website before the submission deadline April 30, 2027, under the article type name "VSI: AI for whom by whom". You can refer to the official call for papers for further details:
https://www.sciencedirect.com/special-issue/331277/ai-for-whom-and-by-whom-cultural-bias-and-the-institutional-and-social-shaping-of-large-language-models
For questions and inquiries feel free to contact the guest editors: Beatrice Orlando (Lead Guest Editor), University of Ferrara, (Beatrice.orlando@unife.it), Thierry Burger-Helmchen, University of Strasbourg/BETA (burger@unistra.fr), Janet Rafner, University of Southern Denmark (jraf@sam.sdu.dk) and Marcus Wagner, University of Augsburg (marcus.wagner@uni-a.de)
------------------------------
Marcus Wagner
University of Augsburg
------------------------------