PRIVACY-BY-DESIGN IN EMOTION AI: DATA PROTECTION FRAMEWORKS AND COMPLIANCE STRATEGIES

Background: The rapid advancement of Emotion Artificial Intelligence (Emotion AI) has created significant opportunities for innovation across a broad variety of domains, including healthcare, marketing, and humancomputer interaction. Emotion AI applications—which process, analyse, and respond to human emotions—rely heavily on sensitive personal data, resulting in privacy and ethical concerns. The implementation of Privacy-by-Design (PbD) principles within such systems is essential to counter these challenges and maintain compliance with changing legal frameworks. This paper discusses the interplay between PbD and Emotion AI, with a special emphasis on the privacy risks associated with the collection and processing of emotional data. The study is set against the broader background of developing ethical AI, emphasising the urgent need to balance technological innovation with robust privacy protection.Methods: The paper provides a conceptual legal analysis of the intersection between Privacy-by-Design (PbD) and Emotion AI within modern data protection frameworks. It employs a comprehensive review of primary sources, including the EU GDPR,the EU AI Act, CJEU and ECtHR jurisprudence, and guidance from Data Protection Authorities, alongside secondary sources like scholarly works and books. The discussion is structured first to provide an overview of Emotion AI, its applications, as well as individual privacy concerns it raises. This is followed by a consideration of existing data protection regimes and how they can be transferred to Emotion AI systems. The study then focuses on the fundamental principles of PbD, examining how they can be applied when developing and deploying Emotion AI technologies.Results and Conclusions: The analysis demonstrates that implementing PbD principles in Emotion AI systems is essential—not merely beneficial—for protecting users’ privacy and ensuring legal compliance. Properly implemented PbD frameworks deliver three essential benefits: enhanced system transparency, stronger accountability mechanisms, and greater user control over their own data. These findings contribute significantly to the theoretical foundations of responsible AI design while offering actionable implementation guidance for organisations deploying Emotion AI systems. Finally, the study presents an unambiguous model for developers and organisations to successfully ride the wave of convergence between emotional intelligence technology and privacy regulations.