57 citations · 60 across the 5 of their papers we have counts for
5 papers
The Case for "Thick Evaluations" of Cultural Representation in AI
Rida Qadri, Mark Diaz, Ding Wang +1
Generative AI model outputs have been increasingly evaluated for their (in)ability to represent non-Western cultures. We argue that these evaluations often operate through reductiv…
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…
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…
STAR: SocioTechnical Approach to Red Teaming Language Models
Laura Weidinger, John Mellor, Bernat Guillen Pegueroles +9
This research introduces STAR, a sociotechnical framework that improves on current best practices for red teaming safety of large language models. STAR makes two key contributions:…
The illusion of artificial inclusion
William Agnew, A. Stevie Bergman, Jennifer Chien +5
Human participants play a central role in the development of modern artificial intelligence (AI) technology, in psychological science, and in user research. Recent advances in gene…