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When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces
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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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Cited by 2 Pith papers
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Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation
Emotion words in text-to-image prompts act as demographic selectors: negative emotions shift outputs toward White, middle-aged, male-coded faces, and young Black women are nearly absent across all models.
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Filters of Identity: AR Beauty and the Algorithmic Politics of the Digital Body
AR beauty filters function as technologies of algorithmic governance that enforce racialized, gendered, and ableist beauty standards while concealing their own influence.
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