Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gestures

• URL: http://arxiv.org/abs/2502.17710v1
• Date: Mon, 24 Feb 2025 23:10:08 GMT
• Title: Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gestures
• Authors: Akhila Yerukola, Saadia Gabriel, Nanyun Peng, Maarten Sap,
• Abstract summary: Gestures are an integral part of non-verbal communication, with meanings that vary across cultures.<n>As AI systems become more integrated into global applications, ensuring they do not inadvertently perpetuate cultural offenses is critical.<n>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.
• Score: 67.89565097372751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• AI as a deliberative partner fosters intercultural empathy for Americans but fails for Latin American participants [4.641694322213005]
  We examined how different types of AI interaction, i.e. deliberative versus non-deliberative and culturally aligned versus non-aligned, affect intercultural empathy. Results show that deliberative conversations increased intercultural empathy among American participants but not Latin American participants. Despite explicit prompting and instruction to represent cultural perspectives in participants' native languages, AI systems still exhibit significant disparities in cultural representation.
  arXiv  Detail & Related papers  (2025-04-04T17:15:01Z)
• Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems [0.4218593777811082]
  Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance. We propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities.
  arXiv  Detail & Related papers  (2025-03-05T06:43:59Z)
• CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
  Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding.<n>CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types.<n>We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
  arXiv  Detail & Related papers  (2025-01-02T14:42:37Z)
• Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation [71.59208664920452]
  Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks.<n>We show that progress on MMLU depends heavily on learning Western-centric concepts, with 28% of all questions requiring culturally sensitive knowledge.<n>We release Global MMLU, an improved MMLU with evaluation coverage across 42 languages.
  arXiv  Detail & Related papers  (2024-12-04T13:27:09Z)
• Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models [4.771099208181585]
  LLMs are increasingly deployed in global applications, ensuring users from diverse backgrounds feel respected and understood.<n>Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values.<n>We present two key contributions: A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and a culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators.
  arXiv  Detail & Related papers  (2024-10-15T18:13:10Z)
• Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
  This paper introduces a novel approach for massively multicultural knowledge acquisition. Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages. Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
  arXiv  Detail & Related papers  (2024-02-14T18:16:54Z)
• Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates [4.857640117519813]
  We argue that cultural and psychological factors play a vital role in the cognitive processing of offensiveness. We demonstrate substantial cross-cultural differences in perceptions of offensiveness. Individual moral values play a crucial role in shaping these variations.
  arXiv  Detail & Related papers  (2023-12-11T22:12:20Z)
• Cultural Bias and Cultural Alignment of Large Language Models [0.9374652839580183]
  We conduct a disaggregated evaluation of cultural bias for five widely used large language models. All models exhibit cultural values resembling English-speaking and Protestant European countries. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.
  arXiv  Detail & Related papers  (2023-11-23T16:45:56Z)
• Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models [89.94270049334479]
  This paper identifies a cultural dominance issue within large language models (LLMs) LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T05:38:23Z)
• Benchmarking Machine Translation with Cultural Awareness [50.183458829028226]
  Translating culture-related content is vital for effective cross-cultural communication. Many culture-specific items (CSIs) often lack viable translations across languages. This difficulty hinders the analysis of cultural awareness of machine translation systems.
  arXiv  Detail & Related papers  (2023-05-23T17:56:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.