Privacy emotion recognition in artificial intelligence: Research frontiers, key challenges, and prospects

Area of Science:

• Artificial Intelligence
• Affective Computing
• Human-Computer Interaction

Background:

• The rise of artificial intelligence (AI) necessitates advancements in emotional intelligence for improved human-machine interaction.
• Understanding and quantifying human psychological states through psychophysiological measures is key to developing empathetic AI.
• User privacy concerns and boundary management are critical in the era of intelligent systems.

Purpose of the Study:

• To examine the evolution of privacy and emotion within AI.
• To explore user privacy imagination and boundary management in AI.
• To develop a theoretical model for privacy paradox factors and a privacy boundary collaboration mechanism.

Main Methods:

• Quantifying psychological states via psychophysiological calculations.
• Multimodal fusion techniques to optimize emotional state inference.
• Constructing theoretical models for privacy paradox and boundary collaboration.

Main Results:

• Revealed contradictions in individual privacy imagination and management within intelligent societies.
• Optimized emotional state inference through multimodal fusion.
• Identified applications in speech recognition, personalized teaching, and public safety.

Conclusions:

• AI offers significant prospects for emotional intelligence applications.
• Addressing individual privacy imagination and management is crucial for intelligent societies.
• Further research is needed on intelligent privacy emotion, including its challenges and future directions.