Multi-Group Proportional Representation for Text-to-Image Models

TL;DR

This paper introduces the Multi-Group Proportional Representation (MPR) metric to systematically measure and improve demographic diversity in text-to-image generative models, addressing representational harms.

Contribution

It proposes a novel MPR framework and an optimization algorithm to enhance demographic balance in T2I model outputs, filling a gap in responsible AI evaluation.

Findings

MPR effectively measures intersectional group representation in generated images.

Using MPR as a training objective improves demographic balance.

Models optimized with MPR maintain high image quality.

Abstract

Text-to-image (T2I) generative models can create vivid, realistic images from textual descriptions. As these models proliferate, they expose new concerns about their ability to represent diverse demographic groups, propagate stereotypes, and efface minority populations. Despite growing attention to the "safe" and "responsible" design of artificial intelligence (AI), there is no established methodology to systematically measure and control representational harms in image generation. This paper introduces a novel framework to measure the representation of intersectional groups in images generated by T2I models by applying the Multi-Group Proportional Representation (MPR) metric. MPR evaluates the worst-case deviation of representation statistics across given population groups in images produced by a generative model, allowing for flexible and context-specific measurements based on user requirements. We also develop an algorithm to optimize T2I models for this metric. Through experiments, we demonstrate that MPR can effectively measure representation statistics across multiple intersectional groups and, when used as a training objective, can guide models toward a more balanced generation across demographic groups while maintaining generation quality.