2025/02/24 by Akhila Yerukola, Yerukola, Akhila, Saadia Gabriel +5 · 1 voice · 2 citations
Computer Science · Psychology · #Child and Animal Learning Development #Cultural diversity #Cultural sensitivity #Gesture #Nonverbal communication #Offensive #Set (abstract data type) #Software deployment #cs.AI #cs.CL #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2502.17710
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Gestures are an integral part of non-verbal communication, with meanings that vary across cultures, and misinterpretations that can have serious social and diplomatic consequences. As AI systems become more integrated into global applications, ensuring they do not inadvertently perpetuate cultural offenses is critical. To this end, we introduce Multi-Cultural Set of Inappropriate Gestures and Nonverbal Signs (MC-SIGNS), a dataset of 288 gesture-country pairs annotated for offensiveness, cultural significance, and contextual factors across 25 gestures and 85 countries. Through systematic evaluation using MC-SIGNS, we uncover critical limitations: text-to-image (T2I) systems exhibit strong US-centric biases, performing better at detecting offensive gestures in US contexts than in non-US ones; large language models (LLMs) tend to over-flag gestures as offensive; and vision-language models (VLMs) default to US-based interpretations when responding to universal concepts like wishing someone luck, frequently suggesting culturally inappropriate gestures. These findings highlight the urgent need for culturally-aware AI safety mechanisms to ensure equitable global deployment of AI technologies.