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Affective Computing in the Age of <monospace>GenAI</monospace>: Balancing Risks and Rewards
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DOI：10.1109/mci.2026.3678879.png)
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Abstract
En 中文
The convergence of Generative Artificial Intelligence (GenAI) and Affective Computing (AffComp) opens new frontiers in creation of emotionally intelligent systems, with growing relevance for human–machine interaction across domains such as virtual assistants, avatars, gaming, and personalised digital experiences. In this paper, we explore the opportunities arising from the integration of GenAI with AffComp across various application areas—data augmentation and synthesis, emotion generation, personalised content creation, data imputation, and emotion transfer and style manipulation—with a particular focus on the deployment of emotionally intelligent systems through a risk–reward balancing framework. The study adopts an empirical and evaluative approach, leveraging state-of-the-art GenAI models within a multimodal AffComp framework to demonstrate significant improvements in emotional expressiveness, adaptive behavior, and personalized responses, enhancing human–computer interaction in applications such as virtual assistants, avatars, and gaming. However, this convergence introduces privacy, ethical, and security risks. Mitigating these risks requires privacy-preserving techniques, real-time bias detection, transparent and culturally diverse models, strong ethical frameworks, user education, and interdisciplinary collaboration to support responsible deployment, ongoing evaluation, and robust oversight. By balancing the associated risks and rewards, we outline strategies to maximise the benefits of GenAI and AffComp while mitigating the potential harms. This paper invites the research community to collaborate on shaping the future of emotionally intelligent AI systems, prioritising both innovation and the well-being of individuals and society as a whole.
Keywords:
Affective computing
Generative AI
Risk management
Human computer interaction
Intelligent systems
Privacy
Ethics
Security
Data privacy
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