Vision-Language Models Suppress Female Representations Under Ambiguous Input
Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succeed. Far less is known about ambiguous inputs (a worker in full gear, a figure seen from behind), cases common in practice yet rarely studied. We find that minimal prompting pressure exposes occupation-gender defaults when prompting ambiguous input images, with models collapsing to male even for strongly female-stereotyped occupations. But do these outputs reflect what models actually encode internally? We introduce LALS (Latent Association Leaning Score), a zero-shot metric that projects visual-token activations into the model's text-embedding space ...
| Authors | Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 31 August 2026 |
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