Dear all, We invite you to submit an abstract to our open panel “Deep Learning and Culture” at the STS NL 2026 Conference <https://www.utwente.nl/en/bms/sts-nl2026/> (University of Twente, 15-17 April 2026). The panel aims to investigate the productive potentials emerging at the encounter between deep learning and culture. Please find the detailed panel description below. In case of interest, we would love to receive your abstract. Please submit your 200-300 words abstract by 07 January 2026 (here: https://www.utwente.nl/en/bms/sts-nl2026/submission/). Decisions will be communicated shortly after. Feel free to get in touch with us should you have any questions about the panel. We look forward to receiving your abstracts! All the best, Louis Ravn (University of Amsterdam), Anna Schjøtt Hansen (University of Amsterdam), Dasha Simons (University of Amsterdam), Paula Helm (Goethe University Frankfurt), and Tobias Blanke (University of Amsterdam) ********** PANEL DESCRIPTION: “Deep Learning and Culture” The recent successes of deep learning technologies, including generative artificial intelligence systems, are premised upon deeply cultural elements: training datasets reflecting various forms of cultural activity (language, art, etc.), new cultural techniques like prompting and fine-tuning, massive material investments underpinned by cultures of innovation, and technoscientific practices of AI development. Flipping around this equation, it is equally true that contemporary culture, broadly conceived, has become increasingly suffused by the operations of deep learning, whether in the fields of media and arts, work, education, medicine, or even warfare. In short, the contemporary relationship between deep learning and culture is one of co-constitution – or even entanglement: they crucially depend on and shape each other. Despite these complex entanglements, scholarship has depicted deep learning as unidirectionally homogenizing culture. While we acknowledge this, we also want to foreground the productive potentials emerging at the encounter between deep learning and culture, potentials that warrant more research to put them onto solid empirical and theoretical foundations. We ask: How can the charged encounter between deep learning and culture help us think about both in new and surprising ways? What happens when deep-learning systems process cultural materials replete with sociopolitical frictions? To what extent can cultural materials be represented by the operations of deep learning systems? How can the methods and epistemologies of deep learning advance critical accounts of culture? To investigate these questions, we invite contributions from scholars researching the nexus of deep learning and culture drawing from the fields of STS, critical AI studies, digital humanities, media studies, anthropology of technology, and related fields. Specifically, we are interested in contributions that present case studies, methodological approaches, and theoretical frameworks that help us problematize the nexus between deep learning and culture in new ways. We explicitly welcome interdisciplinary approaches to this. Thus, we warmly invite the following contributions (but not limited to): * Case studies examining the frictions that emerge at the intersection of deep learning and culture * Methodologies to investigate frictions at the nexus of deep learning and culture (qualitative, computational, quali-quantitative, etc.) * Theoretical approaches foregrounding the entanglements between deep learning and culture (e.g., to what extent is “Deep Culture” a productive framework?) * Empirical investigations into the ways in which deep learning and culture are co-shaping each other and what this implies for different groups of people