@inproceedings{nejadgholi-etal-2026-defining,
title = "Defining Cultural Capabilities for {AI} Evaluation: A Taxonomy Grounded in Intercultural Communication Theory",
author = "Nejadgholi, Isar and
Kianpour, Masoud and
Vishnubhotla, Krishnapriya and
Molamohammadi, Maryam",
editor = "Akhtar, Mubashara and
Batzner, Jan and
Choshen, Leshem and
Ghosh, Avijit and
Gohar, Usman and
Mickel, Jennifer and
Pant, Ichhya and
Talat, Zeerak and
Lin, Michelle",
booktitle = "Proceedings of the Workshop on Evaluating Evaluations ({E}val{E}val)",
month = jul,
year = "2026",
address = "San Diego, CA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.evaleval-1.26/",
pages = "161--173",
ISBN = "979-8-89176-429-3",
abstract = "Tremendous efforts have been put into evaluating the inclusivity and effectiveness of AI systems across cultures. However, the cultural capabilities considered in much of the literature remain vaguely defined, are referred to using interchangeable terminology, and are typically limited to recalling accurate information about various demographics, regions, and nationalities. To address this construct ambiguity, we draw from Intercultural Communication scholarship and propose a three-level taxonomy of AI-relevant cultural capabilities: Cultural Awareness answers ``Does the model know?'', Cultural Sensitivity answers ``How does it frame its knowledge?'', and Cultural Competence answers ``Can it adapt as the interaction evolves?''. Beyond conceptual clarification, we position this taxonomy as a practical tool for improving the validity and interpretability of AI evaluation in real-world, multicultural settings. Without such construct clarity, evaluation results risk overstating model capabilities and may lead to inappropriate deployment decisions in culturally sensitive contexts."
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<abstract>Tremendous efforts have been put into evaluating the inclusivity and effectiveness of AI systems across cultures. However, the cultural capabilities considered in much of the literature remain vaguely defined, are referred to using interchangeable terminology, and are typically limited to recalling accurate information about various demographics, regions, and nationalities. To address this construct ambiguity, we draw from Intercultural Communication scholarship and propose a three-level taxonomy of AI-relevant cultural capabilities: Cultural Awareness answers “Does the model know?”, Cultural Sensitivity answers “How does it frame its knowledge?”, and Cultural Competence answers “Can it adapt as the interaction evolves?”. Beyond conceptual clarification, we position this taxonomy as a practical tool for improving the validity and interpretability of AI evaluation in real-world, multicultural settings. Without such construct clarity, evaluation results risk overstating model capabilities and may lead to inappropriate deployment decisions in culturally sensitive contexts.</abstract>
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%0 Conference Proceedings
%T Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory
%A Nejadgholi, Isar
%A Kianpour, Masoud
%A Vishnubhotla, Krishnapriya
%A Molamohammadi, Maryam
%Y Akhtar, Mubashara
%Y Batzner, Jan
%Y Choshen, Leshem
%Y Ghosh, Avijit
%Y Gohar, Usman
%Y Mickel, Jennifer
%Y Pant, Ichhya
%Y Talat, Zeerak
%Y Lin, Michelle
%S Proceedings of the Workshop on Evaluating Evaluations (EvalEval)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA
%@ 979-8-89176-429-3
%F nejadgholi-etal-2026-defining
%X Tremendous efforts have been put into evaluating the inclusivity and effectiveness of AI systems across cultures. However, the cultural capabilities considered in much of the literature remain vaguely defined, are referred to using interchangeable terminology, and are typically limited to recalling accurate information about various demographics, regions, and nationalities. To address this construct ambiguity, we draw from Intercultural Communication scholarship and propose a three-level taxonomy of AI-relevant cultural capabilities: Cultural Awareness answers “Does the model know?”, Cultural Sensitivity answers “How does it frame its knowledge?”, and Cultural Competence answers “Can it adapt as the interaction evolves?”. Beyond conceptual clarification, we position this taxonomy as a practical tool for improving the validity and interpretability of AI evaluation in real-world, multicultural settings. Without such construct clarity, evaluation results risk overstating model capabilities and may lead to inappropriate deployment decisions in culturally sensitive contexts.
%U https://aclanthology.org/2026.evaleval-1.26/
%P 161-173
Markdown (Informal)
[Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory](https://aclanthology.org/2026.evaleval-1.26/) (Nejadgholi et al., EvalEval 2026)
ACL