1 citations · 1 across the 3 of their papers we have counts for
8 papers
MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignment
Eunkyu Park, Wesley Hanwen Deng, Cheyon Jin +8
Vision-Language Models (VLMs) continue to struggle to make morally salient judgments in multimodal and socially ambiguous contexts. Prior works typically rely on binary or pairwise…
Seeing Twice: How Side-by-Side T2I Comparison Changes Auditing Strategies
Matheus Kunzler Maldaner, Wesley Hanwen Deng, Jason I. Hong +2
While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and utility. A small but growing…
PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming
Wesley Hanwen Deng, Sunnie S. Y. Kim, Akshita Jha +4
Recent developments in AI governance and safety research have called for red-teaming methods that can effectively surface potential risks posed by AI models. Many of these calls ha…
"I Don't Think RAI Applies to My Model'' -- Engaging Non-champions with Sticky Stories for Responsible AI Work
Nadia Nahar, Chenyang Yang, Yanxin Chen +4
Responsible AI (RAI) tools -- checklists, templates, and governance processes -- often engage RAI champions, individuals intrinsically motivated to advocate ethical practices, but…
MIRAGE: Multi-model Interface for Reviewing and Auditing Generative Text-to-Image AI
Matheus Kunzler Maldaner, Wesley Hanwen Deng, Jason Hong +2
While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and usability in different appli…
Vipera: Towards systematic auditing of generative text-to-image models at scale
Yanwei Huang, Wesley Hanwen Deng, Sijia Xiao +3
Generative text-to-image (T2I) models are known for their risks related such as bias, offense, and misinformation. Current AI auditing methods face challenges in scalability and th…