A demographically diverse annotation dataset shows that safety perceptions for text-to-image outputs vary by rater identity and that conventional safety classifiers under-detect bias harms flagged by minority-group raters.
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Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models
A demographically diverse annotation dataset shows that safety perceptions for text-to-image outputs vary by rater identity and that conventional safety classifiers under-detect bias harms flagged by minority-group raters.