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.
A bias-correction for Cramér's V and Tschuprow's T
2 Pith papers cite this work. Polarity classification is still indexing.
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FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.
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A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data
FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.