{
  "id": 3612,
  "url": "https://arxiv.org/abs/2605.28098v1",
  "title": "Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems",
  "summary": "Multi-agent systems are increasingly deployed to support various tasks where agents interact to achieve individual and collective objectives. Although these systems can enhance task performance and decision-making, fairness preservation through bias reduction remains challenging. This study examines how agent-level biases shift and impact system-wide fairness. We use prompts to expose individual agents to group-favoring bias, then assess downstream impacts at the system level. To quantify the im",
  "authors": "Zejian Eric Wu, Zhongyi Jiang, Yuan Zhuang, Paul Jen-Hwa Hu",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T07:53:29.000Z",
  "fetched_at": "2026-07-14T16:30:23.246Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3612",
  "original_url": "https://arxiv.org/abs/2605.28098v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}