Explicable Artificial Intelligence for Affective Computing

Artificial intelligence (AI) is increasingly tasked with recognizing and responding to human emotions, making affective computing one of its most consequential frontiers. As AI spreads into finance, policymaking, and mental health, the opacity of deep learning models raises urgent challenges for trust, accountability, and ethics. This special issue addresses explicability not just as algorithmic transparency, but as a paradigm integrating cognitive science, the humanities, and ethical foresight with technical innovation. Guided by the “Seven Pillars for the Future of AI”— multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness—it envisions affective AI as a partner in meaning-making rather than a mere inference engine. The six featured articles span topics from depression detection and sentiment analysis to hate speech moderation and interpretable driving behaviors, advancing affective AI that is accurate, interpretable, and aligned with human dignity.

Authors

• Rui Mao Nanyang Technological University, Singapore
• Erik Cambria Nanyang Technological University, Singapore
• Yang Li
• Newton Howard Massachusetts Institute of Technology

Keywords

• Affective Computing
• Mental Health
• Deep Learning
• Humanitarian
• Similarity Measure
• Human Values
• Sentiment Analysis
• Human Dignity
• Artificial Intelligence Systems
• Hate Speech
• Field Of Artificial Intelligence
• Inference Tools
• Detection Of Depression
• Future Artificial Intelligence
• Resource For Scholars
• Extensive Experiments
• Data Augmentation
• Online Courses
• Self-supervised Learning
• Intelligent Transportation
• Massive Open Online Courses
• Open Online Courses
• Few-shot Learning
• Augmentation Strategy
• Chinese Online