DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity

Published: 02 Jan 2024, Last Modified: 02 Apr 2024Accepted by TMLREveryoneRevisionsBibTeX
Event Certifications: iclr.cc/ICLR/2024/Journal_Track
Abstract: The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to understand their potential biases. In this work, we introduce three indicators to evaluate the realism, diversity and prompt-generation consistency of text-to-image generative systems when prompted to generate objects from across the world. Our indicators complement qualitative analysis of the broader impact of such systems by enabling automatic and efficient benchmarking of geographic disparities, an important step towards building responsible visual content creation systems. We use our proposed indicators to analyze potential geographic biases in state-of-the-art visual content creation systems and find that: (1) models have less realism and diversity of generations when prompting for Africa and West Asia than Europe, (2) prompting with geographic information comes at a cost to prompt-consistency and diversity of generated images, and (3) models exhibit more region-level disparities for some objects than others. Perhaps most interestingly, our indicators suggest that progress in image generation quality has come at the cost of real-world geographic representation. Our comprehensive evaluation constitutes a crucial step towards ensuring a positive experience of visual content creation for everyone. Code is available at https://github.com/facebookresearch/DIG-In/.
Certifications: Featured Certification
Submission Length: Long submission (more than 12 pages of main content)
Code: https://github.com/facebookresearch/DIG-In
Assigned Action Editor: ~Fuxin_Li1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 1457