Cross-cultural communication often results in misaligned norms and expectations, leading to misunderstandings or harm.As the internet increasingly facilitates cross-cultural communication online, such misalignments also increase.However, there is an opportunity to use Large Language Models (LLMs) to detect such misunderstandings and assist in addressing them.To that end, this study investigates whether cross-cultural norm violations can be detected and mitigated using popular LLMs.Using a set of carefully constructed cross-cultural communication scenarios, half of which present norm violations, we test the ability of OpenAI's GPT-4 Omni (GPT-4o) model to identify cross-cultural communication norm violations.We find that GPT-4o classification accuracy varies by the stated age, gender, and nationality of the communicators described in the scenarios, suggesting a lack of fairness and a potential cultural gap in GPT-4o's detection.

While virtual character embodiment has been studied as a mitigator of singular societal biases in fully immersive VR and empathy games, there have been no major studies on representation featuring standard game play or intersectional identities. In our study, participants played a short 2D video game with racial and gender character manipulations. They then rated a LinkedIn profile application to examine interactions of racial and gender biases. White male participants showed bias against black and female applicants, with the black female applicant experiencing both racial and gender bias. However, participants who embodied certain underrepresented characters in the game displayed reduced biases. Participants’ perceived identification with the characters moderated this effect. The study highlights a lack of homogeneity in the prevalence and potential reduction of different societal biases and incorporates intersectionality to illustrate how multiple parts of a player character’s identity can be used to combat biases.