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    "story_id": "mp-2026-10-23-002",
    "source_story_id": "tmp-feature-selective-ai-auditing",
    "edition_id": "mp-2026-10-23-morning-0105",
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    "section": "safety-security",
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    "headline": "The Auditor Does Not Need to Read Everything",
    "slug": "the-auditor-does-not-need-to-read-everything",
    "dek": "A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.",
    "summary": "A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.",
    "body_text": "AI-assisted decisions often arrive with confidence scores, but reviewing every proposal defeats much of the automation. Researchers formulate selective auditing as an online constrained optimization problem: accept some decisions automatically, send others to an expert, and keep the fraction of AI errors that escape review below a user-chosen threshold. Their algorithms make no distributional assumption, and one is asymptotically optimal for both adversarial and independent data streams under the paper's formulation. Experiments on harmful-request screening, qualitative text annotation and data labeling reduced expert review compared with baselines while maintaining the specified error control. The guarantee applies to the defined sequential setting and its feedback, not to every deployment or an unknowable confidence score.",
    "why_it_matters": "A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.",
    "limitations": [
      "The guarantee applies to the defined sequential setting and its feedback, not to every deployment or an unknowable confidence score."
    ],
    "importance": 10,
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    "first_published_at": "2026-10-23T09:00:00.000-04:00",
    "modified_at": "2026-10-23T09:00:00.000-04:00",
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        "text": "A selective-review policy minimizes expert checks while controlling how many AI errors escape unaudited, without assuming a particular data distribution.",
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        "qualification": "The guarantee applies to the defined sequential setting and its feedback, not to every deployment or an unknowable confidence score."
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    "source_ids": [
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    "tags": [
      "AI auditing",
      "human oversight",
      "error control",
      "confidence"
    ],
    "image_url": "https://themachinepress.com/issues/2026-10-23/feature-selective-auditing.png",
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-10-23-002",
      "title": "arXiv preprint 2610.31662, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.31662v1",
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      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-10-22T13:59:22.000-04:00",
      "accessed_at": "2026-10-23T08:21:38.024-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Auditor Does Not Need to Read Everything",
    "publisher": "The Machine Press",
    "published_at": "2026-10-23T09:00:00.000-04:00",
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