Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classification

TL;DR

This study reveals how text-to-image generative AI and gender classification systems encode aesthetic biases and gender disparities, reflecting societal stereotypes and amplifying inequalities through systematic algorithmic lookism.

Contribution

It uncovers the presence of aesthetic bias and gender bias in AI systems, highlighting systematic lookism as an infrastructural issue in AI vision technologies.

Findings

Generative AI models associate attractiveness with positive attributes.

Gender classification algorithms show higher misclassification rates for women's faces.

Newer models exhibit increased aesthetic constraints and bias amplification.

Abstract

This paper examines algorithmic lookism-the systematic preferential treatment based on physical appearance-in text-to-image (T2I) generative AI and a downstream gender classification task. Through the analysis of 26,400 synthetic faces created with Stable Diffusion 2.1 and 3.5 Medium, we demonstrate how generative AI models systematically associate facial attractiveness with positive attributes and vice-versa, mirroring socially constructed biases rather than evidence-based correlations. Furthermore, we find significant gender bias in three gender classification algorithms depending on the attributes of the input faces. Our findings reveal three critical harms: (1) the systematic encoding of attractiveness-positive attribute associations in T2I models; (2) gender disparities in classification systems, where women's faces, particularly those generated with negative attributes, suffer substantially higher misclassification rates than men's; and (3) intensifying aesthetic constraints in newer models through age homogenization, gendered exposure patterns, and geographic reductionism. These convergent patterns reveal algorithmic lookism as systematic infrastructure operating across AI vision systems, compounding existing inequalities through both representation and recognition. Disclaimer: This work includes visual and textual content that reflects stereotypical associations between physical appearance and socially constructed attributes, including gender, race, and traits associated with social desirability. Any such associations found in this study emerge from the biases embedded in generative AI systems-not from empirical truths or the authors' views.