Human-Centered AI: Designing Emotion-Aware Systems for Safe Human–Machine Interaction
DOI:
https://doi.org/10.15662/IJRAI.2022.0506019Keywords:
Human-centered AI, emotion-aware systems, multimodal emotion recognition, human–machine interaction, safety-aware AI, affective computing, affective computing trust, explainability, autonomous systemsAbstract
The rapid integration of Artificial Intelligence (AI) into daily life—from autonomous vehicles and healthcare assistants to social robots and intelligent conversational agents—has elevated the importance of designing systems that can understand, interpret, and appropriately respond to human emotions. Human-centered AI emphasizes not only technological efficiency but also emotional intelligence, ethical design, and safe interaction protocols. This research explores a comprehensive framework for developing emotion-aware systems that enhance user experience, improve decision-making safety, and foster trust in human–machine collaboration.
Emotion-aware AI systems leverage multimodal data—including facial expressions, speech prosody, body gestures, physiological signals, and contextual cues—to infer the emotional states of users in real time. However, practical deployment remains challenging due to factors such as emotional ambiguity, cultural variation, sensor noise, privacy constraints, and the unpredictability inherent in human behavior. This research proposes a unified architecture that integrates multimodal emotion recognition, context-driven adaptation, and safety-aware response generation to create robust human-centered AI systems capable of functioning reliably across diverse environments.
The proposed framework incorporates an AI-driven perception module utilizing deep learning-based signal processing, transformer models for multimodal fusion, and probabilistic reasoning for handling uncertainty in emotional interpretation. A dynamic adaptation layer then adjusts system behavior—such as voice tone, response timing, decision thresholds, or intervention strategies—based on both emotional cues and task context. The safety module ensures that system actions align with ethical principles and user well-being by enforcing constraints, detecting anomalies, and preventing risky behaviors. Together, these components enable highly interactive AI systems that maintain emotional sensitivity while guaranteeing operational safety.
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