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.
@article{arxiv.2506.11025,
title = {When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces},
author = {Miriam Doh and Aditya Gulati and Matei Mancas and Nuria Oliver},
journal= {arXiv preprint arXiv:2506.11025},
year = {2025}
}
Comments
Accepted as an extended abstract at the Fourth European Workshop on Algorithmic Fairness (EWAF) (URL: https://2025.ewaf.org/home)