Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI

TL;DR

Affective-CARA is a novel framework that enhances emotionally intelligent human-computer interactions by integrating cultural knowledge graphs, affective annotations, and reinforcement learning to produce culturally sensitive responses.

Contribution

It introduces a knowledge graph driven approach with reinforcement learning for culturally adaptive emotional responses in HCI, addressing bias and personalization.

Findings

Outperforms baseline models in sentiment alignment and cultural adaptation.

Reduces cultural bias by 61% (KL-Divergence: 0.28).

Achieves high cultural semantic density of 9.32/10.

Abstract

Culturally adaptive emotional responses remain a critical challenge in affective computing. This paper introduces Affective-CARA, an agentic framework designed to enhance user-agent interactions by integrating a Cultural Emotion Knowledge Graph (derived from StereoKG) with Valence, Arousal, and Dominance annotations, culture-specific data, and cross-cultural checks to minimize bias. A Gradient-Based Reward Policy Optimization mechanism further refines responses according to cultural alignment, affective appropriateness, and iterative user feedback. A Cultural-Aware Response Mediator coordinates knowledge retrieval, reinforcement learning updates, and historical data fusion. By merging real-time user input with past emotional states and cultural insights, Affective-CARA delivers narratives that are deeply personalized and sensitive to diverse cultural norms. Evaluations on AffectNet, SEMAINE DB, and MERD confirm that the framework consistently outperforms baseline models in sentiment alignment, cultural adaptation, and narrative quality. Affective-CARA achieved a Cultural Semantic Density of 9.32 out of 10 and lowered cultural representation bias by 61% (KL-Divergence: 0.28), demonstrating robust performance in generating ethical, adaptive responses. These findings suggest the potential for more inclusive and empathetic interactions, making Affective-CARA an avenue for fostering culturally grounded user experiences across domains such as cross-cultural communication, mental health support, and education.