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.
The Reasonable Effectiveness of Diverse Evaluation Data
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abstract
In this paper, we present findings from an semi-experimental exploration of rater diversity and its influence on safety annotations of conversations generated by humans talking to a generative AI-chat bot. We find significant differences in judgments produced by raters from different geographic regions and annotation platforms, and correlate these perspectives with demographic sub-groups. Our work helps define best practices in model development -- specifically human evaluation of generative models -- on the backdrop of growing work on sociotechnical AI evaluations.
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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.