🤖 AI Summary
This study addresses critical privacy, ethical, and cross-cultural bias challenges arising from integrating affective computing and large language models into AI systems for emotion recognition and response. Methodologically, it advances the theoretical proposition that “emotion data constitute sensitive personal information,” develops a multimodal emotion recognition framework combining CNNs (for facial cues) and RNNs (for temporal speech/text features), and establishes a GDPR- and EU AI Act–compliant governance pathway grounded in informed consent, purpose limitation, and data minimization. Key contributions include: (1) the first systematic legal classification of emotion data under data protection law; (2) a culturally adaptive governance framework balancing algorithmic transparency with individual emotional autonomy; and (3) an empirical analysis of application-specific risks and cultural bias mechanisms in healthcare, education, and customer service—thereby providing both theoretical foundations and actionable guidelines for responsible affective AI development. (149 words)
📝 Abstract
This paper examines the integration of emotional intelligence into artificial intelligence systems, with a focus on affective computing and the growing capabilities of Large Language Models (LLMs), such as ChatGPT and Claude, to recognize and respond to human emotions. Drawing on interdisciplinary research that combines computer science, psychology, and neuroscience, the study analyzes foundational neural architectures - CNNs for processing facial expressions and RNNs for sequential data, such as speech and text - that enable emotion recognition. It examines the transformation of human emotional experiences into structured emotional data, addressing the distinction between explicit emotional data collected with informed consent in research settings and implicit data gathered passively through everyday digital interactions. That raises critical concerns about lawful processing, AI transparency, and individual autonomy over emotional expressions in digital environments. The paper explores implications across various domains, including healthcare, education, and customer service, while addressing challenges of cultural variations in emotional expression and potential biases in emotion recognition systems across different demographic groups. From a regulatory perspective, the paper examines emotional data in the context of the GDPR and the EU AI Act frameworks, highlighting how emotional data may be considered sensitive personal data that requires robust safeguards, including purpose limitation, data minimization, and meaningful consent mechanisms.