Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
KG-FairDiff is an inference-time framework that uses a knowledge graph to guide prompt refinement and reduce gender, race, age, and intersectional biases in text-to-image generation while preserving semantics.
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Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
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KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation
KG-FairDiff is an inference-time framework that uses a knowledge graph to guide prompt refinement and reduce gender, race, age, and intersectional biases in text-to-image generation while preserving semantics.