Explainable Artificial Intelligence (XAI) for Emotion Detection

Abstract

This chapter delves into the significance of explainable artificial intelligence (XAI) in emotion detection (ED) systems, which aim to provide transparency and interpretability in affective computing. The chapter introduces ED systems, defining their purpose and importance in various industries. Subsequently, the need for XAI in emotion detection is discussed, emphasizing ethical concerns, legal requirements, and user trust. Next, the fundamentals of ED systems are explored, encompassing techniques for emotion recognition via facial expressions, voice tones, and text. The challenges associated with these techniques, including variability in human expressions, cultural differences, and data scarcity, are addressed. Next, explanation methods for ED models, and the popular XAI frameworks are presented and evaluated. Quantitative and qualitative evaluation metrics are employed to assess the effectiveness of XAI in ED. Lastly, three case studies demonstrate the successful application of XAI, and as research evolves, future directions that include advanced explainable ED are discussed.