Human interactions depend on recognizing facial expressions, which convey emotions and intentions. This skill is vital for social cohesion, whether interacting with people we identify with or those we perceive as different. As AI is increasingly embedded in robots, understanding how humans perceive emotions in AI faces becomes crucial for designing effective human-AI interactions. While AI recognizing human emotions is well-researched, little attention has been given to how humans interpret emotions displayed by AI or robot faces. Bridging this gap could enhance social robots in roles like healthcare, education, or customer service by making their emotional expressions more intuitive.

Exploring Human Perception of AI Emotions

One way to investigate this could involve experiments where participants are shown images or videos of human and AI/robot faces displaying emotions like happiness, anger, or sadness. Accuracy, reaction time, and subjective ratings (e.g., how "natural" the expression seems) would be measured. Variations might include:

• Static vs. dynamic expressions (videos might improve recognition).
• Different levels of human-likeness in robot faces.
• Contextual cues (e.g., does a hospital setting affect how sadness is perceived?).

The goal would be to uncover patterns in how humans interpret AI emotions, leading to design principles for more effective robot expressions.

Potential Applications and Stakeholders

This research could benefit:

• Robot designers, who could optimize facial expressions for clearer communication.
• AI researchers, informing the development of expressive virtual assistants.
• Psychologists, advancing understanding of human perception of non-human entities.
• End users, like elderly patients or children, who would interact with more emotionally resonant robots.

Stakeholders might include researchers seeking novel findings, manufacturers aiming to improve product design, and participants curious about AI-human interaction.

Execution and Challenges

A pilot study could start with static images of human and AI faces, using online platforms to recruit participants. If results are promising, follow-ups might test dynamic expressions or contextual variations. Key challenges could include creating realistic AI expressions—solved by using CGI tools or collaborating with manufacturers—and ensuring cultural diversity in participants, addressed through online recruitment. Cultural differences in emotion interpretation could be analyzed by comparing subgroup data.

By systematically studying how humans perceive AI emotions, this project could provide actionable insights for designing robots that communicate more naturally, enhancing their effectiveness in real-world applications.