Problem Description

The Stereotype Content Model (SCM), developed by Fiske et al., classifies stereotypes along the dimensions of warmth and competence. While initially designed for analyzing social perception among human groups, its applicability to non-human entities, such as conversational agents (CAs), presents an intriguing research opportunity. Understanding how warmth and competence can be deliberately influenced in these contexts is critical for designing AI interactions that align with user expectations and trust.

Goal of the Thesis

This thesis explores how the dimensions of warmth and competence within the SCM can be manipulated, through the use of stereotypes (e.g., older people are more warm and less competent, and manager more competent less warm), to influence the perceptions of conversational agents or AI assistants. This involves identifying stereotypes affecting these dimensions and experimenting with their deliberate application. The research seeks to provide actionable insights into how AI interactions can be designed to optimize user perceptions of warmth and competence. The goal is to develop an experiment and conduct it.

Requirements

• Proficiency in Python
• Statistical analysis basics
• Basic knowledge of React.js or a comparable frontend framework.
• The ability to use Git

Sources

• Fiske, S. T., Cuddy, A. J. C., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology, 82(6), 878–902.
• Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11(2), 77–83.