156 citations · 234 across the 11 of their papers we have counts for
12 papers
Who Does What in AI Auditing? Designing Human-AI Collaboration for Auditing Generative AI
Eunkyu Park, Markelle Roesti, Wesley Hanwen Deng +5
AI auditing increasingly incorporates AI agents to expand the scale and breadth of audit coverage, yet little is known about how auditing work should be divided without displacing…
PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
Wesley Hanwen Deng, Mingxi Yan, Sunnie S. Y. Kim +5
Recent developments in AI safety research have called for red-teaming methods that effectively surface potential risks posed by generative AI models, with growing emphasis on how r…
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…
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…
Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice
Wesley Hanwen Deng, Nur Yildirim, Monica Chang +3
An emerging body of research indicates that ineffective cross-functional collaboration -- the interdisciplinary work done by industry practitioners across roles -- represents a maj…
Towards "Anytime, Anywhere" Community Learning and Engagement around the Design of Public Sector AI
Wesley Hanwen Deng, Motahhare Eslami, Kenneth Holstein
Data-driven algorithmic and AI systems are increasingly being deployed to automate or augment decision processes across a wide range of public service settings. Yet community membe…
Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice
Wesley Hanwen Deng, Bill Boyuan Guo, Alicia DeVrio +3
Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problema…
Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors
Hong Shen, Alicia DeVos, Motahhare Eslami +1
A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impact…