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The Reasonable Effectiveness of Diverse Evaluation Data

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arxiv 2301.09406 v1 pith:L3P3CGA6 submitted 2023-01-23 cs.HC

Lora Aroyo , Mark Diaz , Christopher Homan , Vinodkumar Prabhakaran , Alex Taylor , Ding Wang This is my paper · ORCID
classification cs.HC
keywords evaluationgenerativeworkai-chatannotationannotationsbackdropbest
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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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Cited by 1 Pith paper

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  1. Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models

    cs.LG 2025-07 conditional novelty 7.0 of 10

    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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