Plurification in/of language technology -- The integration of culture in next-generation AI
For NLP researchers and practitioners, the paper highlights the need for a more reflexive and plural socio-technical approach to integrating culture, but it is primarily a conceptual analysis without empirical results.
The paper proposes that cultural alignment in NLP requires plural epistemologies rather than just adding more examples, and uses a socio-technical model to analyze current approaches, finding that most remain partial and fail to address deeper issues of power and governance.
The paper explores how "culture" can be operationalised in Natural Language Processing (NLP) and what this reveals about the possibilities and limits of considering a plurality of cultural backgrounds in technological design. It proposes that cultural alignment cannot be achieved only by adding more examples of "other cultures", rather it requires plural epistemologies: allowing multiple, locally grounded ways of knowing. To analyze how this plurality of knowing can be addressed in NLP, the paper uses a socio-technical model of language technology (LT) design, the five layers of technological activity model, for collecting and systematizing approaches to culture in NLP. The analysis shows that while NLP research has made progress toward more culturally sensitive systems, many approaches remain partial, addressing "culture" primarily at the level of output or representation while leaving deeper questions of power, governance, and social context unresolved. The paper concludes that operationalising culture requires much more than technical adaptation; it suggests a reflexive and plural socio-technical approach that navigates potentials and limits of computational formalisation for accounting multiple linguistic and socio-cultural backgrounds.