{
  "id": 4876,
  "url": "https://arxiv.org/abs/2605.05562v1",
  "title": "Socio-Conformal Calibration in Complex Survey Data: Marginal Validity Is Not Enough for Subgroup Reliability",
  "summary": "Machine-learning systems used in survey-based social measurement require uncertainty estimates that are reliable across population subgroups, not merely valid in aggregate. We study ordinal conformal prediction for five-level AI-attitude forecasting on the Pew American Trends Panel (Wave 152; n=4,591; 12 race x education subgroups), comparing standard split conformal, Mondrian (group-specific) conformal, and a regularized Mondrian comparator across 100 respondent-disjoint splits with survey-weig",
  "authors": "Amir Rafe, Subasish Das",
  "category": "research",
  "topics": "children-education",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-05-07T01:10:48.000Z",
  "fetched_at": "2026-07-14T16:31:21.931Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4876",
  "original_url": "https://arxiv.org/abs/2605.05562v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}