Draw an Ugly Person An Exploration of Generative AIs Perceptions of Ugliness

TL;DR

This study critically examines how generative AI models perceive and depict ugliness, revealing persistent social biases and stereotypical representations despite efforts for inclusivity.

Contribution

It provides a detailed analysis of biases in AI-generated images and language related to ugliness, highlighting the need for more ethical and inclusive AI training methods.

Findings

AI models associate ugliness with old white male figures.

Biases reflect entrenched social stereotypes and paradoxical projections.

Physical markers like asymmetry and aging are central visual motifs.

Abstract

Generative AI does not only replicate human creativity but also reproduces deep-seated cultural biases, making it crucial to critically examine how concepts like ugliness are understood and expressed by these tools. This study investigates how four different generative AI models understand and express ugliness through text and image and explores the biases embedded within these representations. We extracted 13 adjectives associated with ugliness through iterative prompting of a large language model and generated 624 images across four AI models and three prompts. Demographic and socioeconomic attributes within the images were independently coded and thematically analyzed. Our findings show that AI models disproportionately associate ugliness with old white male figures, reflecting entrenched social biases as well as paradoxical biases, where efforts to avoid stereotypical depictions of marginalized groups inadvertently result in the disproportionate projection of negative attributes onto majority groups. Qualitative analysis further reveals that, despite supposed attempts to frame ugliness within social contexts, conventional physical markers such as asymmetry and aging persist as central visual motifs. These findings demonstrate that despite attempts to create more equal representations, generative AI continues to perpetuate inherited and paradoxical biases, underscoring the critical work being done to create ethical AI training paradigms and advance methodologies for more inclusive AI development.