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

TL;DR

This paper systematically audits eight state-of-the-art synthetic face generation models to reveal persistent demographic and emotion-driven biases across different cultural and linguistic contexts, highlighting fairness and transparency concerns.

Contribution

It provides a comprehensive cross-cultural analysis of demographic biases in synthetic face generation influenced by emotional prompts, using advanced bias metrics.

Findings

All models exhibit demographic biases regardless of origin.

Emotion-conditioned biases significantly affect demographic representation.

Biases persist across Western and Chinese models.

Abstract

Synthetic face generation has rapidly advanced with the emergence of text-to-image (T2I) and of multimodal large language models, enabling high-fidelity image production from natural-language prompts. Despite the widespread adoption of these tools, the biases, representational quality, and cross-cultural consistency of these models remain poorly understood. Prior research on biases in the synthetic generation of human faces has examined demographic biases, yet there is little research on how emotional prompts influence demographic representation and how models trained in different cultural and linguistic contexts vary in their output distributions. We present a systematic audit of eight state-of-the-art T2I models comprising four models developed by Western organizations and four developed by Chinese institutions, all prompted identically. Using state-of-the-art facial analysis algorithms, we estimate the gender, race, age, and attractiveness levels in the generated faces. To measure the deviations from global population statistics, we apply information-theoretic bias metrics including Kullback-Leibler and Jensen-Shannon divergences. Our findings reveal persistent demographic and emotion-conditioned biases in all models regardless of their country of origin. We discuss implications for fairness, socio-technical harms, governance, and the development of transparent generative systems.