{
  "id": 474,
  "url": "https://arxiv.org/abs/2606.29280v1",
  "title": "Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning",
  "summary": "We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without task-specific training, they recommend action when a hindsight-optimal oracle policy mandates inaction. In a six-arm ablation on the Open University Learning Analytics Dataset (N=800 students, four temporal cutoffs), at day 56 -- when the oracle designates 70.1% of students as needing no intervention -- zero-shot GPT-4o recommends action for 73%, a ",
  "authors": "Craig Atkinson",
  "category": "research",
  "topics": "bias-fairness,regulation,children-education,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-28T08:58:17.000Z",
  "fetched_at": "2026-07-14T14:14:32.649Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/474",
  "original_url": "https://arxiv.org/abs/2606.29280v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}