  Our research investigates the impact of Generative Artificial Intelligence
(GAI) models, specifically text-to-image generators (T2Is), on the
representation of non-Western cultures, with a focus on Indian contexts.
Despite the transformative potential of T2Is in content creation, concerns have
arisen regarding biases that may lead to misrepresentations and
marginalizations. Through a community-centered approach and grounded theory
analysis of 5 focus groups from diverse Indian subcultures, we explore how T2I
outputs to English prompts depict Indian culture and its subcultures,
uncovering novel representational harms such as exoticism and cultural
misappropriation. These findings highlight the urgent need for inclusive and
culturally sensitive T2I systems. We propose design guidelines informed by a
sociotechnical perspective, aiming to address these issues and contribute to
the development of more equitable and representative GAI technologies globally.
Our work also underscores the necessity of adopting a community-centered
approach to comprehend the sociotechnical dynamics of these models,
complementing existing work in this space while identifying and addressing the
potential negative repercussions and harms that may arise when these models are
deployed on a global scale.
