Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory
Researchers propose a structured framework for measuring cultural competence in AI systems, moving beyond surface-level demographic knowledge toward interaction-aware adaptation. The taxonomy distinguishes three layers: awareness (factual cultural knowledge), sensitivity (how models frame that knowledge), and competence (dynamic adjustment during conversations). This work addresses a critical gap in AI evaluation methodology, where cultural capabilities have been poorly defined and inconsistently measured across the industry. For practitioners building multilingual or cross-cultural systems, the framework offers concrete evaluation criteria that go deeper than accuracy metrics alone, potentially reshaping how teams benchmark fairness and inclusivity.58
