Social Perception

Social perception research investigates how individuals and machines interpret and respond to social cues, aiming to understand the underlying mechanisms and biases in these processes. Current research focuses on analyzing social biases in AI models like LLMs and vision-language models, often employing techniques such as sentiment analysis, transformer-based architectures, and multimodal data analysis to assess perceptions of attributes like age, gender, and race. These findings have significant implications for mitigating bias in AI systems and improving human-robot interaction, as well as for understanding human social dynamics and developing more socially aware technologies.