{
  "id": 3422,
  "url": "https://arxiv.org/abs/2605.31556v1",
  "title": "Vision-Language Models Suppress Female Representations Under Ambiguous Input",
  "summary": "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",
  "authors": "Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-29T17:20:02.000Z",
  "fetched_at": "2026-07-14T16:30:14.370Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3422",
  "original_url": "https://arxiv.org/abs/2605.31556v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}