3 citations · 10 across the 11 of their papers we have counts for
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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…
What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research
Wesley Hanwen Deng, Agathe Balayn, Andrew Selbst +6
Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public. How industry practitioners interpret, implement, and sustain RAI work direct…
What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them
Megan Li, Wendy Bickersteth, Ningjing Tang +7
Despite growing concerns about the risks of Generative AI (GenAI), there is limited understanding of public perceptions of these risks and their associated failure modes -- defined…
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…
Vipera: Blending Visual and LLM-Driven Guidance for Systematic Auditing of Text-to-Image Generative AI
Yanwei Huang, Wesley Hanwen Deng, Sijia Xiao +4
Despite their increasing capabilities, text-to-image generative AI systems are known to produce biased, offensive, and otherwise problematic outputs. While recent advancements have…
What Comes After Harm? Mapping Reparative Actions in AI through Justice Frameworks
Sijia Xiao, Haodi Zou, Alice Qian Zhang +4
As Artificial Intelligence (AI) systems are integrated into more aspects of society, they offer new capabilities but also cause a range of harms that are drawing increasing scrutin…
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…