Author(s)

Rocco de Filippis^1*, Abdullah Al Foysal²

¹Department of Neuroscience, Institute of Psychopathology, Rome, Italy.
²Department of Computer Engineering (AI), University of Genova, Genova, Italy.

Understanding human emotions has historically posed significant challenges due to the complexities inherent in the human psyche. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide promising tools to unravel these complexities. This paper explores the evolving landscape of emotional intelligence, emphasizing the crucial role of AI and ML in enhancing our understanding of emotional dynamics. It specifically examines how AI technologies are currently used to recognize emotions across diverse domains such as marketing and healthcare, highlighting the shift from traditional single-modality systems to more sophisticated multimodal approaches. This transition allows for more nuanced analysis of emotional patterns, integrating facial expressions with other indicators like vocal intonations and physiological responses. The paper also addresses critical ethical concerns, including biases in AI algorithms and privacy issues, that arise with the deployment of these technologies. Furthermore, it discusses the application of AI and ML in providing mental health support, where these technologies facilitate personalized interventions. Despite notable advancements, persistent challenges remain in accurately interpreting complex emotional data. The paper speculates on future directions and potential breakthroughs, backed by real-world case studies that demonstrate the transformative impact of AI and ML in decoding human emotions. Through this exploration, we reflect on the significant progress and continuing evolution of emotional intelligence technology.

Emotion Intelligence, AI, Machine Learning, Decoding Emotions, Multimodal Approaches, Ethical Considerations

Filippis, R. and Foysal, A. (2024) Decoding Emotions: How AI and Machine Learning Unravel the Human Psyche. Voice of the Publisher, 10, 382-399. doi: 10.4236/vp.2024.104030.