{
  "id": 17348,
  "url": "https://arxiv.org/abs/2608.06123v1",
  "title": "Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts",
  "summary": "Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, and legal reasoning that are difficult to capture with a single metric. In this work, we introduce Poli-Bias, a counterfactual framework for measuring whether LLMs treat legally equivalent conflict scenarios differently depending on the countries involved. Poli-Bias compares responses to paired prompts in which country identities are systematically",
  "authors": "Massi-Nissa Abboud, Aladin Djuhera, Elena Cabrio, Holger Boche",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T14:54:30.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "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/17348",
  "original_url": "https://arxiv.org/abs/2608.06123v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}