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 re

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ethics.ai (29 May 2026), “Vision-Language Models Suppress Female Representations Under Ambiguous Input,” evidence record 3422, https://ethics.ai/record/3422 (originally published by arXiv).

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