Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory
For AI researchers and practitioners evaluating models in culturally sensitive contexts, this taxonomy provides a structured framework to clarify what cultural capabilities mean, reducing the risk of overstating model performance.
The paper addresses the vague and inconsistent definitions of cultural capabilities in AI evaluation by proposing a three-level taxonomy (Cultural Awareness, Sensitivity, Competence) grounded in intercultural communication theory. The taxonomy aims to improve the validity and interpretability of AI evaluations in multicultural settings.
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.