Inspecting the Geographical Representativeness of Images from Text-to-Image Models

TL;DR

This study evaluates how well popular text-to-image models like DALL.E 2 and Stable Diffusion represent different geographical regions in their generated images, revealing biases and areas for improvement.

Contribution

It introduces a crowdsourced methodology to measure geographical bias in generated images and analyzes the impact of specifying country names on representativeness.

Findings

Generated images predominantly reflect US and Indian surroundings.

Specifying country names improves geographical representativeness.

Many countries remain underrepresented despite input specifications.

Abstract

Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentation. Given their outsized impact, it is important to ensure that the generated content reflects the artifacts and surroundings across the globe, rather than over-representing certain parts of the world. In this paper, we measure the geographical representativeness of common nouns (e.g., a house) generated through DALL.E 2 and Stable Diffusion models using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India, and the top generations rarely reflect surroundings from all other countries (average score less than 3 out of 5). Specifying the country names in the input increases the representativeness by 1.44 points on average for DALL.E 2 and 0.75 for Stable Diffusion, however, the overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive. Lastly, we examine the feasibility of quantifying the geographical representativeness of generated images without conducting user studies.