Eschner, JohannesLabadie-Tamayo, RobertoZeppelzauer, MatthiasWaldner, ManuelaAigner, WolfgangAndrienko, NataliaWang, Bei2025-05-262025-05-2620251467-8659https://doi.org/10.1111/cgf.70135https://diglib.eg.org/handle/10.1111/cgf70135Bias 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 zeroshot 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.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visualization; Computing methodologies → Artificial intelligenceHuman centered computing → VisualizationComputing methodologies → Artificial intelligence10.1111/cgf.7013520 pages