{
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  "dataset": "The Calibrated Authority Index",
  "version": "2026-09-08",
  "creator": "Chris Huber Reitz",
  "license": "CC-BY-4.0",
  "id": "ama",
  "name": "American Medical Association — Augmented Intelligence Development, Deployment, and Use in Health Care",
  "segment": "medical-association",
  "url": "https://www.ama-assn.org/system/files/ama-ai-principles.pdf",
  "scores": {
    "D1": 2,
    "D2": 2,
    "D3": 2,
    "D4": 2,
    "D5": 2,
    "D6": 2
  },
  "ca": 12,
  "posture": "Balanced",
  "c2_fit": "fits",
  "c3": "Both-split",
  "twilight": true,
  "coded_date": "2026-08-09",
  "last_changed": null,
  "revision": 0,
  "quote": "Clinical decisions influenced by AI must be made with specified qualified human intervention points during the decision-making process. A qualified human is defined as a licensed physician with the necessary qualifications and training to independently provide the same medical service without the aid of AI.",
  "quote_label": "Recommendation 1(g), General Governance (November 2024)",
  "provenance": {
    "url": "https://www.ama-assn.org/system/files/ama-ai-principles.pdf",
    "verify_status": "primary-live-2026-08-09",
    "note": "Second load-bearing line, Rec. 2(b): 'AI tools or systems cannot augment, create, or otherwise generate records, communications, or other content on behalf of a physician without that physician's consent and final review.' D4 anchored on Rec. 2(d) ('Where patient-facing content is generated by AI, the use of AI in generating that content should be disclosed or otherwise noted within the content'), Rec. 3(a)(viii)(2) ('Constraint to evidence-based outcomes and mitigation of \"hallucination\"/\"confabulation\" or other output error'), and Rec. 8(e) ('requiring the exclusion of AI systems as authors'). Twilight framing is explicit and repeated: 'there is not yet any clear legal standard for determining liability' and 'Given that there are no regulations or generally accepted standards or frameworks to govern the design, development, and deployment of generative AI' — precedent collapse; plus AI model drift/degradation and post-market surveillance — feedback delay. c2_fit 'fits': the reserve line is drawn by harm potential AND named verification difficulty — Rec. 4(b)(i) governs 'lack of ability to readily verify the accuracy of responses or the sources used to generate the response.'"
  },
  "jsonld": {
    "@context": "https://schema.org",
    "@type": "Review",
    "@id": "https://calibrated-authority.chrishuberreitz.com/institutions/ama",
    "url": "https://calibrated-authority.chrishuberreitz.com/institutions/ama",
    "name": "Calibrated Authority rating — American Medical Association — Augmented Intelligence Development, Deployment, and Use in Health Care",
    "datePublished": "2026-08-09",
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      "@type": "CreativeWork",
      "name": "American Medical Association — Augmented Intelligence Development, Deployment, and Use in Health Care — public generative-AI policy",
      "url": "https://www.ama-assn.org/system/files/ama-ai-principles.pdf",
      "text": "Clinical decisions influenced by AI must be made with specified qualified human intervention points during the decision-making process. A qualified human is defined as a licensed physician with the necessary qualifications and training to independently provide the same medical service without the aid of AI.",
      "abstract": "Clinical decisions influenced by AI must be made with specified qualified human intervention points during the decision-making process. A qualified human is defined as a licensed physician with the necessary qualifications and training to independently provide the same medical service without the aid of AI.",
      "alternateName": "Recommendation 1(g), General Governance (November 2024)"
    },
    "reviewRating": {
      "@type": "Rating",
      "ratingValue": 12,
      "bestRating": 12,
      "worstRating": 0,
      "ratingExplanation": "Composite Calibrated Authority score (sum of six 0-2 dimensions). Breakdown — D1 Traceability & inspectability: 2; D2 Human authorship & accountability: 2; D3 Disclosure & labeling: 2; D4 Synthetic-identity / fabrication prohibition: 2; D5 Human validation in loop: 2; D6 Evidential-trust emphasis: 2. Posture: Balanced. Verification-boundary fit: fits. Trust-logic: Both-split."
    },
    "author": {
      "@type": "Person",
      "name": "Chris Huber Reitz",
      "url": "https://chrishuberreitz.com",
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    "publisher": {
      "@type": "Person",
      "name": "Chris Huber Reitz",
      "url": "https://chrishuberreitz.com"
    },
    "isPartOf": {
      "@type": "Dataset",
      "name": "The Calibrated Authority Index",
      "url": "https://calibrated-authority.chrishuberreitz.com",
      "version": "2026-09-08",
      "license": "https://creativecommons.org/licenses/by/4.0/"
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    "license": "https://creativecommons.org/licenses/by/4.0/"
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}