Demographic Bias in T2I Models

T2I: Text-to-Image

Overview

This field studies how text-to-image systems represent people across attributes such as gender, race, age, disability, and culture, because generated images can reinforce stereotypes or erase groups. Audits compare outputs for occupations, neutral and explicit demographic prompts, varied contexts and languages, sometimes against workforce statistics or community-informed standards. Mitigation approaches include prompt rewriting, embedding and attention steering at inference time, and fairness-aware guidance or training. A central challenge is defining fair representation for each setting: reducing measured bias can compromise prompt fidelity, while simple benchmarks may miss intersectional or context-specific harms.

Latest papers

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Jul 26, 2024

She Works, He Works: A Curious Exploration of Gender Bias in AI-Generated Imagery

She Works, He Works is an interdisciplinary project that combines feminist visual theory, AI critique, and artistic practice to examine gender representation in AI-generated imagery. By juxtaposing text-to-image outputs of male and female construction workers, it reveals how societal biases manifest in generative AI systems. The study employs critically crafted prompts that blend traditionally gendered traits with unconventional contexts, exposing disparities in representation-emphasizing aesthetics and agility for women versus authority and professionalism for men. Grounded in Griselda Pollock's feminist insights on visual culture, the project positions AI as both a technological and cultural actor, capable of perpetuating stereotypes and enabling critique. As both a study and an artwork, the project underscores the transformative potential of AI to reimagine gender norms and inspire equitable narratives. It highlights the ethical implications of AI in creative industries, calling for transparent practices, diverse datasets, and reflexive engagement with AI's cultural impact. Through its integration of critical analysis and artistic exploration, She Works, He Works contributes to broader discussions on inclusive AI development and the role of art in fostering societal change. This dual approach showcases the power of AI as a medium for critique and a tool for envisioning more just and inclusive futures.