Architecture Matters: Gender Disparities in Automated Image Moderation
| Autorzy | Zawadzka A.; Głomb P. |
|---|---|
| Tytuł | Architecture Matters: Gender Disparities in Automated Image Moderation |
| Czasopismo | PP-RAI'2026: 7th Polish Conference on Artificial Intelligence |
| Rok | 2026 |
| Status | Published |
| Abstrakt | <p>Automated image moderation systems shape online visibility and dataset curation, yet prior work has identified demographic disparities in similarity-based NSFW classifiers. We compare such systems with instruction-tuned vision-language models (VLMs) that generate structured moderation decisions with textual rationales. Using the PHASE-annotated subset of the GCC dataset, we compute false positive rates overall and by gender. <br>Results show substantial variation in moderation strictness and in bias direction: two models show more pronounced strictness in moderation of male images, while other two exhibit higher removal rates for female images. <br>Reasoning-based models do not consistently mitigate bias but, in the proposed pipeline, they offer a transparency layer.</p> |