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arXiv · 2506.11025

When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces

Miriam Doh·Aditya Gulati·Matei Mancas·Nuria Oliver
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Abstract

This paper examines how synthetically generated faces and machine learning-based gender classification algorithms are affected by algorithmic lookism, the preferential treatment based on appearance. In experiments with 13,200 synthetically generated faces, we find that: (1) text-to-image (T2I) systems tend to associate facial attractiveness to unrelated positive traits like intelligence and trustworthiness; and (2) gender classification models exhibit higher error rates on "less-attractive" faces, especially among non-White women. These result raise fairness concerns regarding digital identity systems.

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Miriam Doh, Aditya Gulati, Matei Mancas, Nuria Oliver. 2025-05-20. When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces. https://arxiv.org/abs/2506.11025

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