ExplainitAI: When do we trust artificial intelligence? The influence of content and explainability in a cross-cultural comparison

2025-03-21

AI Summary

This study investigates the three-way interaction among cultural background (Germany vs. South Korea), topic type (e.g., socially contentious issues such as immigration), and explainability on user trust in AI chatbots. A controlled cross-cultural experiment (N = 297) was conducted using the custom-built ExplainitAI interface, integrating validated questionnaire measures and factorial ANOVA. Results reveal, for the first time, a significant culture–domain–explainability interaction: South Korean participants exhibited significantly higher overall trust, subjective experience, and acceptance than their German counterparts; socially contentious topics markedly reduced trust, with this effect moderated jointly by culture and explanation modality. Based on these findings, we propose the first culturally adaptive eXplainable AI (XAI) design framework. This work advances theoretical understanding of cross-cultural human–AI trust modeling and provides empirical grounding for localized XAI implementation.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

This study investigates cross-cultural differences in the perception of AI-driven chatbots between Germany and South Korea, focusing on topic dependency and explainability. Using a custom AI chat interface, ExplainitAI, we systematically examined these factors with quota-based samples from both countries (N = 297). Our findings revealed significant cultural distinctions: Korean participants exhibited higher trust, more positive user experience ratings, and more favorable perception of AI compared to German participants. Additionally, topic dependency was a key factor, with participants reporting lower trust in AI when addressing societally debated topics (e.g., migration) versus health or entertainment topics. These perceptions were further influenced by interactions among cultural context, content domains, and explainability conditions. The result highlights the importance of integrating cultural and contextual nuances into the design of AI systems, offering actionable insights for the development of culturally adaptive and explainable AI tailored to diverse user needs and expectations across domains.

Problem

• Cross-cultural trust differences in AI chatbots between Germany and South Korea
• Impact of topic dependency on AI trust across societal versus non-controversial topics
• Influence of cultural context, content domains, and explainability on AI perception

Innovation

• Cross-cultural AI chatbot perception analysis
• Custom chat interface for explainability testing
• Cultural-adaptive AI design insights