Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914396737896448 |
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| author | Eschner, Johannes Labadie-Tamayo, Roberto Zeppelzauer, Matthias Waldner, Manuela |
| author_facet | Eschner, Johannes Labadie-Tamayo, Roberto Zeppelzauer, Matthias Waldner, Manuela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models Eschner, Johannes Labadie-Tamayo, Roberto Zeppelzauer, Matthias Waldner, Manuela Human-Computer Interaction 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. |
| title | Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.19703 |