7 papers
Positive Alignment: Artificial Intelligence for Human Flourishing
Ruben Laukkonen, Seb Krier, Chloé Bakalar +13
Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psych…
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…
Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups
Charvi Rastogi, Tian Huey Teh, Pushkar Mishra +10
AI systems crucially rely on human ratings, but these ratings are often aggregated, obscuring the inherent diversity of perspectives in real-world phenomenon. This is particularly…