Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models
For researchers and practitioners addressing caste bias in AI, this provides a more nuanced understanding of discrimination mechanics, though it is an incremental extension of existing fairness work.
This work shifts from treating caste as a category to examining its relational aspects in Text-to-Image models, revealing nuanced biases beyond upper vs lower-caste binaries through algorithmic audit and critical discourse analysis.
Text-to-Image (T2I) models have shown promising utility across various domains. However, such models are also amplifying harmful societal biases in their outputs. In the context of South Asia, recent work has shown caste biases and stereotypes are being perpetuated through Generative AI (GenAI) systems. While this research offers extremely relevant insight into invisibilized narratives of caste discrimination through the GenAI system, they often treat caste as an identity category. Therefore, in this work we shift our ontology to focus on the relational aspect of caste. This enables us to develop a more nuanced understanding of the mechanics of caste discrimination by and through T2I models. Combining an algorithmic audit with critical discourse analysis, we draw on a conceptual frame challenging Brahminical Normativity to show how caste biases are perpetuated beyond the simple binaries of upper vs lower-caste categories. Our contributions are two-fold. Beyond challenging the categorical understanding of caste as a category, we propose an anti-caste approach to tackle the issue of caste bias and fairness in AI systems.