Attractive by Design: How The Attractiveness Halo Effect Shapes AI Perception

Humans are impacted by tens of cognitive biases when making decisions. One such bias is the attractiveness halo effect, i.e., the tendency to associate positive traits, such as intelligence or trustworthiness, with individuals that are perceived as attractive. While this bias has been studied extensively in humans, there is limited work studying the existence of such an attractiveness bias in the content generated by AI systems. This work-in-progress paper investigates the presence of the attractiveness halo effect in text-to-image (T2I) generative AI models, specifically examining how T2I models associate “attractiveness” with positive traits, such as intelligence, trustworthiness, and sociability, while linking “unattractiveness” to negative attributes. Through preliminary experiments generating over 12,000 face images labeled with various traits across gender and race categories, we measure the similarity between images associated with attractiveness and other traits by computing centroid distances in the feature embedding space. Initial findings indicate the presence of a halo effect, similar to that observed in humans, where images deemed attractive are more closely associated with positive than with negative traits. These results suggest that T2I models embed an attractiveness bias. However, the extent of these associations varies across demographic groups, with notable differences based on gender and race. This study underscores the potential of generative AI to replicate our own biases, perpetuating societal stereotypes and raising important implications for model development and their application in downstream tasks.