  Recent studies have shown that Text-to-Image (T2I) model generations can
reflect social stereotypes present in the real world. However, existing
approaches for evaluating stereotypes have a noticeable lack of coverage of
global identity groups and their associated stereotypes. To address this gap,
we introduce the ViSAGe (Visual Stereotypes Around the Globe) dataset to enable
the evaluation of known nationality-based stereotypes in T2I models, across 135
nationalities. We enrich an existing textual stereotype resource by
distinguishing between stereotypical associations that are more likely to have
visual depictions, such as `sombrero', from those that are less visually
concrete, such as 'attractive'. We demonstrate ViSAGe's utility through a
multi-faceted evaluation of T2I generations. First, we show that stereotypical
attributes in ViSAGe are thrice as likely to be present in generated images of
corresponding identities as compared to other attributes, and that the
offensiveness of these depictions is especially higher for identities from
Africa, South America, and South East Asia. Second, we assess the stereotypical
pull of visual depictions of identity groups, which reveals how the 'default'
representations of all identity groups in ViSAGe have a pull towards
stereotypical depictions, and that this pull is even more prominent for
identity groups from the Global South. CONTENT WARNING: Some examples contain
offensive stereotypes.
