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.
and Behrend, Tara S
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
AI researchers should take greater responsibility for publicly explaining the limitations of their technologies to prevent misuse in high-stakes applications such as emergency translation services.
citing papers explorer
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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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LLMs in the Real World: Evaluating "AI" in Emergency Contexts
AI researchers should take greater responsibility for publicly explaining the limitations of their technologies to prevent misuse in high-stakes applications such as emergency translation services.