mlsanityData to 14 Oct 2026

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

arXiv 2503.19252 ↗First posted 25 Mar 2025cs.HCcited by 4

On the map: LLM safety and security → Vision-language and text-to-image safety

Abstract (arXiv)

While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and usability in different applications. Recent years have seen growing interest in engaging diverse AI users in auditing generative AI that might impact their lives. To this end, we propose MIRAGE as a web-based tool where AI users can compare outputs from multiple AI text-to-image (T2I) models by auditing AI-generated images, and report their findings in a structured way. We used MIRAGE to conduct a preliminary user study with five participants and found that MIRAGE users could leverage their own lived experiences and identities to surface previously unnoticed details around harmful biases when reviewing multiple T2I models' outputs compared to reviewing only one.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Vision-language and text-to-image safety, one of 12 directions in LLM safety and security. Builds on 5 of its 11 references that are in the map. First posted on arXiv 25 Mar 2025.

Builds on

5 of 11 references.

References · 5

  1. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  2. 2021Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviorscited by 50
  3. 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 34
  4. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 32
  5. 2023Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebookcited by 22

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025An Exploration of Default Images in Text-to-Image Generationcited by 1
  2. 2025Seeing Twice: How Side-by-Side T2I Comparison Changes Auditing Strategiescited by 0
  3. 2026MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignmentcited by 0
  4. 2026PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AIcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Finding the Subjective Truth: Collecting 2 Million Votes for Comprehensive Gen-AI Model Evaluation
  2. 2025Mirage: Unveiling Hidden Artifacts in Synthetic Images with Large Vision-Language Models
  3. 2025MIRAGE: Towards AI-Generated Image Detection in the Wild
  4. 2024GenAI Arena: An Open Evaluation Platform for Generative Models
  5. 2024Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models
  6. 2024Manipulating and Mitigating Generative Model Biases without Retraining
  7. 2026Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
  8. 2024Would Deep Generative Models Amplify Bias in Future Models?