Emotion Recognition Artificial Intelligence and the Psychology of Human Affect: A Critical Examination of Theory, Technology, Bias, and the African Context

This paper presents a conceptual analysis of Emotion Recognition Artificial Intelligence (ER- AI), integrating psychological theory, technical architecture, and cultural bias evaluation with a specific focus on African contexts. It introduces the CBEAT Framework (Cultural Bias Evaluation for Affective Technologies) as a structured approach for assessing bias in affective computing systems Emotion Recognition Artificial Intelligence (ER-AI), also referred to as Affective Computing, represents one of the most rapidly advancing and simultaneously contested frontiers at the intersection of psychology and machine learning. By training computational systems to detect, interpret, and respond to human emotional states through facial expressions, vocal patterns, physiological signals, and textual cues, ER-AI has moved from theoretical laboratory experiments into real-world deployments across healthcare, education, marketing, employment, and security. This paper undertakes a comprehensive critical examination of Emotion Recognition AI by synthesising foundational psychological theories that underpin the field,principally Ekman's Basic Emotions Theory, the Constructionist Theory of Emotion, Appraisal Theory, and the Facial Action Coding System , with a rigorous technical analysis of how modern ER-AI systems function. The paper further examines documented real-world case studies of ER-AI deployment in healthcare, education, and commercial applications, before conducting a detailed critical analysis of the systemic biases embedded within these systems, with a particular focus on their demonstrably reduced accuracy for individuals of African descent. Drawing on landmark empirical studies from MIT, NIST, Harvard, and Carnegie Mellon University Africa, the paper argues that current ER-AI systems do not merely reflect technical limitations but encode and perpetuate structural inequalities rooted in non-representative training data. The paper concludes with a forward-looking framework of recommendations for the ethical, culturally-sensitive, and inclusive development of Emotion AI, with specific attention to the African context. The central thesis advanced is that Emotion Recognition AI, as currently constituted, represents a profound scientific and ethical challenge: the technology is powerful enough to affect consequential decisions in people's lives, yet insufficiently robust, equitable, or culturally literate to bear that responsibility responsibly , particularly in African and other non-Western contexts. Keywords: Emotion Recognition AI, Affective Computing, Facial Action Coding System, Algorithmic Bias, Cultural Psychology, Human-Centered AI, Africa, Paul Ekman, Machine Learning, Cognitive Psychology