1 citations · 3 across the 4 of their papers we have counts for
4 papers
Harm Amplification in Text-to-Image Models
Susan Hao, Renee Shelby, Yuchi Liu +6
Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outpu…
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images
Hansa Srinivasan, Candice Schumann, Aradhana Sinha +5
Capturing the diversity of people in images is challenging: recent literature tends to focus on diversifying one or two attributes, requiring expensive attribute labels or building…
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting
Preethi Lahoti, Nicholas Blumm, Xiao Ma +8
A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a res…
Fair treatment allocations in social networks
James Atwood, Hansa Srinivasan, Yoni Halpern +1
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been…