  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.
