Target-based prompting lets users define fairness distributions for skin tones in generative AI, shifting outputs closer to chosen targets across 36 tested prompts for occupations and contexts.
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Mod-Guide uses RAG with a community co-created corpus to make LLM moderation responses more contextually accurate for insensitive speech toward Bangladesh's Hindu and Chakma minorities, with mixed-method evaluation showing differences by ethnic background.
The paper reframes caste as relational rather than categorical and combines algorithmic audit with critical discourse analysis to examine nuanced caste biases in T2I models while proposing an anti-caste framework for AI fairness.
citing papers explorer
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Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models
Target-based prompting lets users define fairness distributions for skin tones in generative AI, shifting outputs closer to chosen targets across 36 tested prompts for occupations and contexts.
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Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities
Mod-Guide uses RAG with a community co-created corpus to make LLM moderation responses more contextually accurate for insensitive speech toward Bangladesh's Hindu and Chakma minorities, with mixed-method evaluation showing differences by ethnic background.
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Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models
The paper reframes caste as relational rather than categorical and combines algorithmic audit with critical discourse analysis to examine nuanced caste biases in T2I models while proposing an anti-caste framework for AI fairness.