{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/make-disagreement-between-you-and-ai-a-review-trigger",
  "id": "MHC-D-RESEARCH-0498",
  "version": "0.1.0",
  "title": "Make disagreement between you and AI a review trigger",
  "summary": "Disagreement is useful data when neither side automatically wins.",
  "kind": "protocol",
  "body": "When your judgment and the AI recommendation diverge, pause before switching. Compare evidence, identify the exact point of disagreement, and ask whether one side has task-specific information the other lacks. Route unresolved high-impact disagreement to independent verification.",
  "limits": [
    "Disagreement frequency alone does not identify who is right; verification must remain task-specific."
  ],
  "topics": [
    "union-ai-reliance-metacognition"
  ],
  "intents": [],
  "source_ids": [
    "RS-45C4F121413B315D",
    "RS-3CFE1F643A73D6BC"
  ],
  "evidence": [
    {
      "claim": "The 2026 mathematical model shows that human and AI metacognitive sensitivity jointly affect achievable combined accuracy when confidence is used to combine decisions.",
      "source_id": "RS-45C4F121413B315D",
      "role": "supports",
      "note": "The Bayes-optimal assumptions are stronger than ordinary workplace decision support.",
      "locator": "Analytic results"
    },
    {
      "claim": "A 2026 study found metacognitive estimates of one's own confidence shape responses to AI advice and limited metacognitive sensitivity can produce inconsistent advice-taking.",
      "source_id": "RS-3CFE1F643A73D6BC",
      "role": "supports",
      "note": "Detailed boundary conditions should be interpreted from the full study rather than the abstract alone.",
      "locator": "Abstract highlights"
    }
  ],
  "use_when": [
    "Human and AI judgments conflict on a consequential case."
  ],
  "avoid_when": [
    "Disagreement frequency alone does not identify who is right; verification must remain task-specific."
  ],
  "example": "AI recommends approving a configuration change while the operator knows a dependency is under maintenance; resolve the context mismatch before acceptance.",
  "check": "Consequential disagreement produces evidence inspection rather than automatic deference to either human or AI.",
  "steps": [
    "Which fact or assumption makes our conclusions diverge?",
    "Does the AI have evidence I did not use?",
    "Do I have context the AI cannot see?",
    "Whose confidence signal is validated on this task?",
    "Does the impact justify an independent check?"
  ],
  "sources": [
    {
      "id": "RS-45C4F121413B315D",
      "title": "Modeling the joint impact of human and AI metacognitive sensitivity on human-AI collaboration",
      "url": "https://www.sciencedirect.com/science/article/pii/S0022249626000192"
    },
    {
      "id": "RS-3CFE1F643A73D6BC",
      "title": "AI advice and human metacognition",
      "url": "https://www.sciencedirect.com/science/article/pii/S0167923626001466"
    }
  ],
  "relations": [
    {
      "from": "MHC-D-RESEARCH-0498",
      "to": "MHC-D-RESEARCH-0505",
      "type": "useful_with",
      "url": "/knowledge/route-cases-using-both-human-and-ai-confidence-only-after-both-are-calibrated"
    }
  ],
  "collections": [
    {
      "id": "RC-39B535F35706B375",
      "title": "Calibrate when to rely on AI instead of measuring trust as a feeling",
      "url": "/collections/calibrate-when-to-rely-on-ai-instead-of-measuring-trust-as-a-feeling"
    }
  ]
}