{
  "id": 114,
  "url": "https://arxiv.org/abs/2607.07852v1",
  "title": "False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation",
  "summary": "Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tasks is complicated by a common property of modern segmentation datasets: expert-annotated gold labels are expensive, so abundant machine-generated (silver) labels are added to limit annotation cost. This matters because the reference used to judge a model can itself be biased. In this study, we present the first fairnes",
  "authors": "Linus Juni, Aasa Feragen, Aditya Parikh",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-08T18:30:50.000Z",
  "fetched_at": "2026-07-14T14:14:19.967Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/114",
  "original_url": "https://arxiv.org/abs/2607.07852v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}