4 papers
Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity
Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl +9
Ensuring the safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for analyzing ordinal safe…
Value Profiles for Encoding Human Variation
Taylor Sorensen, Pushkar Mishra, Roma Patel +6
Modelling human variation in rating tasks is crucial for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using n…
"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark DÃaz, Charvi Rastogi +10
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, s…
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models
Charvi Rastogi, Tian Huey Teh, Pushkar Mishra +10
Current text-to-image (T2I) models often fail to account for diverse human experiences, leading to misaligned systems. We advocate for pluralistic alignment, where an AI understand…