{
  "id": 5180,
  "url": "https://arxiv.org/abs/2604.27479v1",
  "title": "Gender Bias in YouTube Exposure: Allocative and Structural Inequalities in Political Information Environments",
  "summary": "Recommendation algorithms have become the dominant mechanism for information distribution on digital platforms, profoundly shaping personalized information consumption environments. However, gender bias, as a significant form of algorithmic discrimination, may cause users to experience unequal exposure within different political information environments. Taking YouTube as a case, we conduct a controlled social-bot field experiment, where male-coded and female-coded profiles are constructed. We t",
  "authors": "Jipeng Tan, Weifeng Zhang, Ye Wu, Jialin Guo, Yong Min",
  "category": "research",
  "topics": "bias-fairness,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-30T06:20:35.000Z",
  "fetched_at": "2026-07-14T16:31:35.573Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5180",
  "original_url": "https://arxiv.org/abs/2604.27479v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}