Changing only the prompter's age and gender in AI coding prompts produces statistically significant differences in generated website interface design, template content, and code structure across 800 generated websites and a 20-person user study.
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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.
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Biased or Personalized? The Impact of Personal Information on AI-driven Development
Changing only the prompter's age and gender in AI coding prompts produces statistically significant differences in generated website interface design, template content, and code structure across 800 generated websites and a 20-person user study.
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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.