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

arXiv: https://arxiv.org/abs/2503.19252 · HTML: https://mlsanity.com/paper/2503.19252/

## 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.

## Placed in

- LLM safety and security / [Vision-language and text-to-image safety](https://mlsanity.com/safety/vision-language-and-text-to-image-safety/)

## Builds on (most cited first, 5 of 5)

- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits](https://mlsanity.com/paper/2205.06922/) (2022)
- [Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice](https://mlsanity.com/paper/2210.03709/) (2022)
- [Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook](https://mlsanity.com/paper/2301.12243/) (2023)

## Built on by (most cited first, 4 of 4)

- [An Exploration of Default Images in Text-to-Image Generation](https://mlsanity.com/paper/2505.09166/) (2025)
- [Seeing Twice: How Side-by-Side T2I Comparison Changes Auditing Strategies](https://mlsanity.com/paper/2511.21547/) (2025)
- [MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignment](https://mlsanity.com/paper/2602.03665/) (2026)
- [PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI](https://mlsanity.com/paper/2605.05682/) (2026)

## 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.