揭示文本生成图像模型中种姓歧视的深层关系机制，提出反种姓干预路径。
Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models
- 从种姓身份转向关系视角，剖析模型如何延续种姓压迫逻辑。
- 通过算法审计与话语分析发现，偏见远超上下等阶二元对立。
- 提出反种姓框架，为公平AI提供社会批判性设计思路。
文本生成图像（T2I）模型在多个领域展现出巨大潜力，但其输出也放大了有害的社会偏见。在南亚语境下，已有研究显示生成式AI系统正传播种姓偏见与刻板印象。然而，这些研究通常将种姓视为身份类别，忽视其结构性关系本质。本文转向种姓的关系性本体论，以更细致理解种姓歧视在T2I模型中的运作机制。结合算法审计与批判话语分析，我们运用挑战婆罗门规范性的理论框架，揭示种姓偏见如何超越上层/下层种姓的简单二元分类而持续存在。本文贡献在于：一方面，批判了将种姓视为静态类别的认知模式；另一方面，提出一种反种姓方法，以应对人工智能系统中的种姓偏见与公平问题。
原文摘要 · Abstract (English)
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.
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