Vipera: Towards systematic auditing of generative text-to-image models at scale
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915197725179904 |
|---|---|
| author | Huang, Yanwei Deng, Wesley Hanwen Xiao, Sijia Eslami, Motahhare Hong, Jason I. Perer, Adam |
| author_facet | Huang, Yanwei Deng, Wesley Hanwen Xiao, Sijia Eslami, Motahhare Hong, Jason I. Perer, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vipera: Towards systematic auditing of generative text-to-image models at scale Huang, Yanwei Deng, Wesley Hanwen Xiao, Sijia Eslami, Motahhare Hong, Jason I. Perer, Adam Human-Computer Interaction 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. |
| title | Vipera: Towards systematic auditing of generative text-to-image models at scale |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.11113 |