Information
- Publication Type: Journal Paper with Conference Talk
- Workgroup(s)/Project(s):
- Date: June 2025
- Journal: Computer Graphics Forum
- Open Access: yes
- Location: Luxembourg
- Lecturer: Johannes Eschner
- Article Number: e70135
- ISSN: 1467-8659
- Event: EuroVis 2025
- DOI: 10.1111/cgf.70135
- Pages: 20
- Publisher: WILEY
- Keywords: Visualization, Bias, Artificial Intelligence
Abstract
Bias in generative Text-to-Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zero-shot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in-depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model-agnostic and publicly available. In four case study interviews, experts in AI and ethics were able to discover visual biases that have so far not been described in literature.Additional Files and Images
Weblinks
- live demo
Live demo of the ViBEx application - Entry in reposiTUm (TU Wien Publication Database)
- DOI: 10.1111/cgf.70135
BibTeX
@article{eschner-2025-ide, title = "Interactive Discovery and Exploration of Visual Bias in Generative Text‐to‐Image Models", author = "Johannes Eschner and Roberto Labadie‐Tamayo and Matthias Zeppelzauer and Manuela Waldner", year = "2025", abstract = "Bias in generative Text-to-Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zero-shot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in-depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model-agnostic and publicly available. In four case study interviews, experts in AI and ethics were able to discover visual biases that have so far not been described in literature.", month = jun, journal = "Computer Graphics Forum", articleno = "e70135", issn = "1467-8659", doi = "10.1111/cgf.70135", pages = "20", publisher = "WILEY", keywords = "Visualization, Bias, Artificial Intelligence", URL = "https://www.cg.tuwien.ac.at/research/publications/2025/eschner-2025-ide/", }