Culturally Responsive AI: Designing Emotion Recognition Systems that Account for Diverse Non-Verbal Communication Styles.

Emotion recognition systems powered by artificial intelligence (AI) have rapidly gained prominence in mental health, education, and human-computer interaction. However, many existing models are built on limited, often Western-centric datasets, which overlook the rich diversity of non-verbal communication across cultures. This paper proposes a culturally responsive AI framework for designing emotion recognition systems that are inclusive of varied facial expressions, gestures, gaze behaviors, and prosodic cues observed in different cultural contexts. Using an interdisciplinary methodology that combines affective computing, cross-cultural psychology, and sociolinguistics, this study analyzes discrepancies in emotional signaling and examines how AI systems can be trained to interpret emotional data in a contextually appropriate way. Findings reveal that culturally insensitive models risk misinterpretation, leading to ethical and practical challenges in real-world applications. The paper offers design principles, ethical considerations, and implementation strategies for developing AI systems that are not only accurate but also equitable and socially aware. This work contributes to ongoing efforts in human-centered AI and highlights the importance of cultural sensitivity in next-generation affective technologies.