When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces
It addresses fairness concerns in digital identity systems, highlighting biases that could impact marginalized groups, though it is incremental as it builds on existing fairness research.
This paper tackled the problem of algorithmic lookism in synthetic face generation and gender classification, finding that text-to-image systems link attractiveness to unrelated positive traits and gender models have higher error rates on less-attractive faces, especially non-White women, with experiments on 13,200 faces.
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.