Foundation models are predominantly trained on English-language and Western-centric data, often marginalizing non-English contexts. While recent scholarship calls for more localized models, there remains limited empirical research on how such models are developed and deployed. This paper examines the sociotechnical dynamics of local model development and deployment in South Korea, where efforts to build “sovereign AI” reflect aspirations for greater autonomy over data, infrastructure, and cultural alignment. Drawing on semi-structured interviews with 15 Korean AI practitioners, we surface key motivations, such as linguistic and cultural specificity, regulatory compliance, and reduced dependence on foreign technologies, that are entangled with broader imaginaries of sovereignty. At the same time, these efforts face constraints including limited GPU access, scarcity of Korean-language data, and reliance on global infrastructures. We argue that AI sovereignty should be understood not as an abstract political principle but as situated practices shaped by opportunities and constraints of local sociotechnical and regulatory contexts.
ACM CHI Conference on Human Factors in Computing Systems