{
  "id": 5701,
  "url": "https://arxiv.org/abs/2604.16790v1",
  "title": "Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering",
  "summary": "Large Language Models are increasingly used as judges to evaluate code artifacts when exhaustive human review or executable test coverage is unavailable. LLM-judge is increasingly relevant in agentic software engineering workflows, where it can help rank candidate solutions and guide patch selection. While attractive for scale, current practice lacks a principled account of reliability and bias: repeated evaluations of the same case can disagree; small prompt edits can swing outcomes; and seemin",
  "authors": "Zixiao Zhao, Amirreza Esmaeili, Fatemeh Fard",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-18T02:35:05.000Z",
  "fetched_at": "2026-07-14T16:31:57.534Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5701",
  "original_url": "https://arxiv.org/abs/2604.16790v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}