8 citations · 16 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Investigating Youth AI Auditing
Jaemarie Solyst, Cindy Peng, Wesley Hanwen Deng +5
Youth are active users and stakeholders of artificial intelligence (AI), yet they are often not included in responsible AI (RAI) practices. Emerging efforts in RAI largely focus on…
WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI
Wesley Hanwen Deng, Wang Claire, Howard Ziyu Han +3
There has been growing interest from both practitioners and researchers in engaging end users in AI auditing, to draw upon users' unique knowledge and lived experiences. However, w…