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most citedMIRAGE: Multi-model Interface for Reviewing and Auditing Generative Text-to-Image AI

1 citations · 1 across the 3 of their papers we have counts for

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cs.HC2025

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…

cs.HC2025

"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…

cs.HC20251 cited

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…

cs.HC2025

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…

cs.HC2021

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…