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This paper applies the anthropological emic versus etic framework to critique current NLP research, arguing that the field's internal "culture" often overlooks diverse cultural perspectives in favor of outsider cataloguing. By examining the full pipeline from task selection to evaluation, the authors propose specific pathways for developing AI systems that are more culturally attuned and epistemologically robust.
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Supporting and Performing Culture from the Inside

arXiv2 min read

arXiv:2609.32281v1 Announce Type: new Abstract: Anthropology and related disciplines which study culture have often found it useful to consider their epistemological and methodological approaches in terms of emic versus etic. This is the distinction between the insider perspective, how the world is viewed by a member of the culture, versus the outsider perspective, the scientific cataloguing of cultural knowledge and practice. We adopt this framework to systematically analyse assumptions in recent research on culture in NLP along the full pipeline of task selection, data collection, system design and evaluation. We draw attention to the `culture of NLP' which shapes the research focus and approaches of our field, and suggest pathways towards a more culturally attuned AI.

Abstract:Anthropology and related disciplines which study culture have often found it useful to consider their epistemological and methodological approaches in terms of emic versus etic. This is the distinction between the insider perspective, how the world is viewed by a member of the culture, versus the outsider perspective, the scientific cataloguing of cultural knowledge and practice. We adopt this framework to systematically analyse assumptions in recent research on culture in NLP along the full pipeline of task selection, data collection, system design and evaluation. We draw attention to the `culture of NLP' which shapes the research focus and approaches of our field, and suggest pathways towards a more culturally attuned AI.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2609.32281 [cs.CL]

(or arXiv:2609.32281v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.32281 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 213 words

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