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

2026-01-18

AI Summary

This study investigates how emotional prompts induce demographic biases—pertaining to gender, age, and race—in synthetic human faces generated by text-to-image models. We conduct a systematic audit of eight prominent text-to-image models from both Western and Chinese contexts, generating facial images using standardized emotional prompts and quantifying deviations from global demographic distributions through facial analysis algorithms and information-theoretic metrics (KL and JS divergence). Our work reveals, for the first time, the significant influence of emotional conditioning on demographic bias in generated outputs and introduces an information-theoretic framework for fairness evaluation. The findings demonstrate that all examined models exhibit pronounced, cross-culturally consistent emotion-driven biases, underscoring a shared challenge in generative AI regarding fairness and transparency.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• demographic bias
• synthetic face generation
• emotion-driven bias
• text-to-image models
• cross-cultural consistency

Innovation

• emotion-driven bias
• synthetic face generation
• demographic representation
• cross-cultural audit
• text-to-image models