  The surge in the popularity of text-to-image generators (T2Is) has been
matched by extensive research into ensuring fairness and equitable outcomes,
with a focus on how they impact society. However, such work has typically
focused on globally-experienced identities or centered Western contexts. In
this paper, we address interpretations, representations, and stereotypes
surrounding a tragically underexplored context in T2I research: caste. We
examine how the T2I Stable Diffusion displays people of various castes, and
what professions they are depicted as performing. Generating 100 images per
prompt, we perform CLIP-cosine similarity comparisons with default depictions
of an 'Indian person' by Stable Diffusion, and explore patterns of similarity.
Our findings reveal how Stable Diffusion outputs perpetuate systems of
'castelessness', equating Indianness with high-castes and depicting
caste-oppressed identities with markers of poverty. In particular, we note the
stereotyping and representational harm towards the historically-marginalized
Dalits, prominently depicted as living in rural areas and always at protests.
Our findings underscore a need for a caste-aware approach towards T2I design,
and we conclude with design recommendations.
