# Vipera: Towards systematic auditing of generative text-to-image models at scale

arXiv: https://arxiv.org/abs/2503.11113 · HTML: https://mlsanity.com/paper/2503.11113/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI](https://mlsanity.com/paper/2605.05682/) (2026)

## Abstract (arXiv)

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 thoroughness, and it is even more challenging to enable auditors to explore the auditing space in a structural and effective way. Vipera employs multiple visual cues including a scene graph to facilitate image collection sensemaking and inspire auditors to explore and hierarchically organize the auditing criteria. Additionally, it leverages LLM-powered suggestions to facilitate exploration of unexplored auditing directions. An observational user study demonstrates Vipera's effectiveness in helping auditors organize their analyses while engaging with diverse criteria.