Recognizing and Responding to Human Emotions: A Survey of Artificial Emotional Intelligence for Cooperative Social Human-Machine Interactions

• Published in: 2023 IEEE Frontiers in Education Conference (FIE) pp. 1 - 5
• Main Authors: Ahmadi, Nicu, Hammond, Tracy
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.10.2023
• Subjects: AEI; Artificial Emotional Intelligence; Bibliographies; Collaboration; Emotion recognition; Human computer interaction; Human-Centered AI; Human-Machine Interactions; Machine learning algorithms; Responsible AI; Surveys; Virtual assistants
• ISSN: 2377-634X
• DOI: 10.1109/FIE58773.2023.10343328

This paper represents a survey of Artificial Emotional Intelligence (AEI) for cooperative social human-machine interactions. Additionally, it explores the potential benefits and challenges of integrating AEI into various applications, including virtual assistants, customer service, and healthcare. To imbue machines with the ability to understand human emotions, interest in AEI has grown to develop machines capable of natural human-machine interactions. The rapid advancement of Artificial Intelligence (AI) has enabled machines to perform tasks that were once exclusive to humans. However, current AI systems cannot understand emotions and social cues, which limits their ability to interact with humans. Social cues refer to the various nonverbal and verbal signals that people use to convey their emotions, intentions, and thoughts during social interactions. These cues include facial expressions, tone of voice, gestures, body language, and other subtle indicators that humans use to communicate with each other. There are several challenges associated with developing AEI systems that can navigate the complexity of human social interactions and emotions. One of the biggest challenges is developing machine learning algorithms that can accurately recognize and interpret human emotions. Emotions are complex, often subtle, and heavily context-dependent. Machines must recognize and respond to them in a way similar to how humans do through contextual, nonverbal, and verbal cues. Another challenge is developing natural language processing algorithms that accurately recognize the sentiment behind spoken or written communication. Sentiment analysis is a challenging problem because language is inherent ambiguity, and the meaning of a word or phrase can depend on situational, historical, and cultural context. Sentiment can depend on subtle factors such as tone of voice, facial expressions, and body language. Despite these challenges, there are also significant opportunities for improving the quality of human-machine interactions. By developing machines that recognize and respond to human emotions, we can achieve the goal of creating more natural and satisfying interactions between humans and machines. This work-in-progress paper is a preliminary analysis of the technical challenges and opportunities of developing AEI and assesses potential implications for human-machine interaction (e.g., human trust in technology). The paper presents future research directions for AEI, highlighting the need for interdisciplinary collaborations to develop a more human-centered and responsible AI system, while surveying the technology and the potential impacts of human trust in technology. In this literature review, the guiding question is: Can a method for AI in combination with interdisciplinary collaborations be developed to take a logical leap to make a determination of a human's emotional state for a more human-centered and responsible AI system.