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AI face generators show demographic and emotion-driven biases

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper titled "Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation" investigates biases in text-to-image models. The study audited eight models, four Western and four Chinese, generating 56,000 faces across seven prompt conditions. Findings indicate a pervasive overrepresentation of young and White-coded individuals, with intersectional analyses revealing significant underrepresentation of specific demographic combinations. Emotionally conditioned prompts, particularly negative ones, were found to shift outputs towards White, middle-aged, male-coded faces, correlating with lower perceived attractiveness. AI

IMPACT Highlights the need for intersectional and emotion-conditioned audits in AI face generation to mitigate demographic biases.

RANK_REASON Research paper published on arXiv detailing biases in AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI face generators show demographic and emotion-driven biases

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Research paper published on arXiv detailing biases in AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mengting Wei, Aditya Gulati, Guoying Zhao, Nuria Oliver ·

    Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation

    arXiv:2602.00032v4 Announce Type: replace-cross Abstract: Synthetic faces from text-to-image (T2I) models pervade digital media, yet their demographic biases under emotionally conditioned prompts remain poorly understood. We aim to systematically audit how emotionally conditioned…

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