{
  "id": 19446,
  "url": "https://arxiv.org/abs/2608.13328v1",
  "title": "It's How You Ask: Gender-Associated Linguistic Bias in LLMs",
  "summary": "Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models. These effects persist after controlling for prompt complexity and feature carry-over. Explicit gender cues like sign-off names are encode",
  "authors": "Katherine Van Koevering, Anjalie Field",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T14:54:23.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19446",
  "original_url": "https://arxiv.org/abs/2608.13328v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}