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  "question": "When should a person review AI-extracted document data?",
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  "topic": "Human review of extracted document fields",
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  "title": "When should a person review AI-extracted document data?",
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  "hook": "Keep a field in human review when its source is missing, its meaning is unresolved, it falls outside the tested document scope or the next action requires approval. A confidence score can help prioritize checks; it does not settle those decisions. Our invented quantity-correction example shows what to send the reviewer and which update must wait.",
  "citableAnswer": "MING proposes reviewing extracted document data against its source, unresolved conflicts, tested scope and permission to act. Send the receiving person the relevant pages, field value and question to resolve, while holding the affected update. The proposed packet explains a review decision; it supplies no universal confidence threshold or measured automation result.",
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  "evidenceNote": "Original illustrative review packet, informed by technical provider guidance. The source excerpts, order and reviewer roles are invented; no OCR or model benchmark was run.",
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  "summary": "Decide which extracted fields need review. Use a quantity-correction example, four review conditions and a source packet for the receiving person.",
  "pillar": "Hybrid Organisation",
  "datePublished": "2026-10-26",
  "dateModified": "2026-10-26",
  "freshness": {
    "updated": "October 2026",
    "nextReview": "November 2026",
    "nextReviewDate": "2026-11-25"
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  "confidence": "B",
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      "title": "Document-field review packet and quantity-correction example",
      "publisher": "MING Labs",
      "date": "26 October 2026",
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        "Separate source reading, correction authority and permission to update",
        "Review packet and four contrasting conditions"
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      "title": "Best Practices — Amazon Textract",
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      "id": "S3",
      "title": "From extraction to action: Using grounding and confidence to build reliable document workflows",
      "publisher": "Microsoft Foundry — Joe Filcik and Colin Murphy",
      "date": "14 October 2026; checked 26 October 2026",
      "url": "https://devblogs.microsoft.com/foundry/content-understanding-confidence-grounding-hitl/",
      "supports": [
        "Check field-level confidence against representative verified documents and a held-out set",
        "The described insurance workflow retained human review of every field"
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      "method": "Provider account and technical method; no supplier performance figures or customer outcomes reused."
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      "q": "What confidence threshold should trigger human review?",
      "a": "There is no universal cutoff. Evaluate the score against checked examples from the actual document types and fields, including mistakes. Set the decision around the consequences of an error and validate it on separate documents. A score cannot remove an approval that the workflow still requires."
    },
    {
      "q": "Should every empty extracted field go to a person?",
      "a": "First distinguish an optional field that is genuinely blank from a required value that is missing or was not extracted. That needs a defined rule and access to the source. In the proposed method, an unresolved required value blocks the dependent update; the reviewer may need a better scan or clarification from the document owner."
    },
    {
      "q": "Can a second model resolve the correction?",
      "a": "It may help locate or compare the text. Agreement between models does not establish who was entitled to change the quantity. If approval evidence is missing, obtain that evidence or an authorized decision instead of treating another extraction as approval."
    },
    {
      "q": "Does accepting a review task release the order update?",
      "a": "No. Acceptance records who has taken responsibility for reviewing it. The field remains unresolved until the reviewer records a supported decision. Updating the order also requires the normal permission and business checks for that action."
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  "contentMarkdown": "---\ntitle: \"When should a person review AI-extracted document data? — MING Labs\"\ndescription: \"Decide which extracted fields need review. Use a quantity-correction example, four review conditions and a source packet for the receiving person.\"\ncanonical: https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data\nlang: en\nlast-updated: 2026-10-26\n---\n\n# When should a person review AI-extracted document data?\n\n[Home](https://www.minglabs.com/)  [Insights](https://www.minglabs.com/insights)  [Answers](https://www.minglabs.com/insights/answers)  When should a person review AI-extracted document data?\n\nAnswer\n\n\n\nKeep a field in human review when its source is missing, its meaning is unresolved, it falls outside the tested document scope or the next action requires approval. A confidence score can help prioritize checks; it does not settle those decisions. Our invented quantity-correction example shows what to send the reviewer and which update must wait.\n\nInvented order Q17 · proposed review\n\n## Send both quantity records to the person who can resolve the correction\n\nForm revision A · page 1\n\n**Quantity: 12**\n\nAttached note · page 2\n\n**Correction: 21**\n\nNo approval record accompanies the correction.\n\nOperations reviewer\n\n**Was the change approved, and which quantity applies?**\n\n**Order update on hold**\n\nAccepting the task is not approval of the quantity.\n\nProposed handling: preserve both source pages and the unresolved question. A reviewer may request missing evidence. The affected update waits for a supported decision and the normal permission checks. No working integration or model accuracy is demonstrated.\n\nLast updated: October 2026 | Next review: November 2026 Proposed MING method [Machine-readable record ](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data.json)\n\nBy MING Labs · Editorial team\n\nThe answer at a glance\n\n1.  [Separate a readable value from an approved change.](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data#correction)\n2.  [Identify which field needs a decision.](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data#conditions)\n3.  [Give the reviewer the source and a specific question.](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data#packet)\n\n##  A readable correction can still leave the quantity unresolved\n\nImagine your intake system has read an order form. The quantity box says 12. A note on the second page says “Correction: 21”, but the packet contains no record approving that change. Before the order system uses either number, someone must decide which quantity applies.\n\nThis is our invented example, shown in the graphic.[\\[S1\\]](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data#source-s1) Both numbers are legible. A better transcription alone cannot establish whether the correction is valid. The operations reviewer needs the original field, the note and the missing approval question together. Asking that person to check only a neatly extracted “21” hides the very reason their judgement is needed.\n\nKeep two decisions separate: what the document says, and what the process permits someone to do with it. Even after an authorized person confirms the corrected quantity, that confirmation does not grant an agent new access to change the order. The update must still follow its normal controls.\n\n## Where does your document review get stuck?\n\nLet’s discuss the field, its source and the handover to the person who can resolve it.\n\n[Discuss my document review](https://www.minglabs.com/contact?type=project&topic=document-field-review)\n\nTell us what you have in mind. We will respond personally.\n\n##  Review the unresolved field before it drives another action\n\nStart with the action that would use the extracted information. In the example, the quantity would change an order record. Hold that particular update while its input is unresolved. Other permitted work, such as recording that the document arrived, need not wait merely because the quantity does.\n\nWe propose the following checks for a document intake process. They are decision conditions to adapt and test with the process owner, not a declaration that all remaining fields are safe to automate.\n\n| What the reviewer faces | Proposed handling |\n| --- | --- |\n| The required quantity is absent from the extraction, and the source has not been checked. | Open the relevant page. Distinguish a missed value, a genuinely blank field and an unreadable scan before using it. |\n| The form says 12 and a correction says 21, with no approval evidence. | Present both locations and ask who can confirm the valid quantity. Hold the order update. |\n| A new form version places the quantity in a different table. | Keep the affected extraction in review until that form and field have been assessed; do not inherit the previous version’s release automatically. |\n| The value is readable and consistent, but changing the order requires authorization. | Keep that approval step. Correct extraction is evidence about a field, not permission to perform the action. |\n\nConfidence can help decide where to look first. AWS advises choosing thresholds around the application and its sensitivity to errors.[\\[S2\\]](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data#source-s2) Microsoft describes checking field scores against representative, verified documents and validating decisions on held-out examples. In its insurance account, every field still received human review; prioritization did not remove that review.[\\[S3\\]](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data#source-s3) We therefore prescribe no percentage here.\n\n##  Send the original pages with the exact question to resolve\n\nA review task should let the receiving person begin without reconstructing the document’s journey. For our quantity example, we would keep the following packet beside the proposed update. Show only information the reviewer is permitted to access, and retain the source version so a later change does not silently replace the evidence they saw.\n\n| Packet item | Invented order Q17 |\n| --- | --- |\n| Source and location | Form revision A, page 1, quantity box: 12. Attached note, page 2: “Correction: 21”. Both excerpts link to their full pages. |\n| Unresolved question | Was the correction approved, and by whom? No approval record accompanies the packet. |\n| Affected action | Changing the quantity in order Q17. That update remains on hold. |\n| Receiving role | The designated operations reviewer, with a named substitute and an agreed response time. |\n| Decision record | The reviewer, time, selected value or continued hold, and the source or authorized clarification supporting that decision. |\n\nThe reviewer may conclude that neither number can yet be used. That is a useful outcome: the next step is to request the missing approval from the responsible person. Do not force the interface to offer only “accept” and “reject” when “cannot decide from these documents” is the accurate answer.\n\n##  Keep the update on hold until the review is actually resolved\n\nSaving the packet in a queue establishes that it is waiting. Record separately when a person accepts the task and when they resolve the field. In Q17, a receipt saying “accepted by operations” still leaves the quantity undecided. Only the later supported decision can supply the value for the next permitted step.\n\nAgree who watches unaccepted tasks and what happens when the reviewer is absent. If the source link fails, the receiving system is unavailable or the response time expires, keep the affected update blocked and make the waiting work visible to the designated backup. Do not convert a timeout into silent approval.\n\nThis page defines the contents and boundaries of a proposed document review. Whether the receiving system actually carries that packet and preserves the hold needs an integration test. Our [production acceptance guide](https://www.minglabs.com/insights/answers/how-do-you-test-an-ai-agent-before-production) covers testing the complete workflow, including failures beyond the model’s text.\n\n##  Test the review with one difficult document\n\nChoose a permitted example with the person who accepts the work. Start with the source pages, identify the exact unresolved field and write the question that person must answer. Then follow the packet through the actual receiving system. Verify that the reviewer can open the evidence, keep the case unresolved and record a decision without releasing an unauthorized update.\n\nRepeat that exercise for missing pages, new form versions and an unavailable reviewer. Record lost evidence, incorrect updates, waiting cases and the time people spend finding or correcting information. The synthetic Q17 example explains what to examine; it cannot establish your extraction accuracy, useful confidence cutoff or staffing needs.\n\nIf the team later wants to reduce routine checks, treat that as a separate release decision with evidence from its actual workload. Our [guide to moving from full review to sampling](https://www.minglabs.com/insights/answers/when-can-you-switch-from-checking-every-ai-agent-output-to-sampling) explains the conditions and what should restore full checks. First make sure the difficult document can reach a person who can resolve it.\n\nWhat this method establishes\n\n-   The order, source excerpts, correction and roles are invented to make a review packet inspectable. They are not actual model outputs or a client deployment.\n-   The four conditions and handover record are MING’s proposed method. Technical sources support task-specific review; they do not validate this example or supply a universal release threshold.\n-   No extraction accuracy, review-time saving or working integration is claimed. Required approvals and access rules remain decisions for the actual organisation.\n\nCitable short version\n\nMING proposes reviewing extracted document data against its source, unresolved conflicts, tested scope and permission to act. Send the receiving person the relevant pages, field value and question to resolve, while holding the affected update. The proposed packet explains a review decision; it supplies no universal confidence threshold or measured automation result.\n\nOriginal illustrative review packet, informed by technical provider guidance. The source excerpts, order and reviewer roles are invented; no OCR or model benchmark was run.\n\n## Which document checks does your workflow need?\n\nDescribe what your system extracts and what happens to those values next. We can discuss the unresolved decisions, supporting evidence and handover to your team.\n\n[Discuss my document review](https://www.minglabs.com/contact?type=project&topic=document-field-review)\n\nTell us what you have in mind. We will respond personally.\n\n[Read the acceptance guide ](https://www.minglabs.com/insights/answers/how-do-you-test-an-ai-agent-before-production)\n\n## Sources\n\n\\[S1\\]\n\nDocument-field review packet and quantity-correction example MING Labs · 26 October 2026 Supports: Separate source reading, correction authority and permission to update, Review packet and four contrasting conditions Original proposed method; invented source excerpts and roles, not customer data, model output or a calibrated release policy.\n\n\\[S2\\]\n\n[Best Practices — Amazon Textract](https://docs.aws.amazon.com/textract/latest/dg/textract-best-practices.html) Amazon Web Services · Checked 26 October 2026 Supports: Confidence thresholds depend on the application and sensitivity to errors Provider technical guidance. Its illustrative thresholds are not adopted as a MING policy.\n\n\\[S3\\]\n\n[From extraction to action: Using grounding and confidence to build reliable document workflows](https://devblogs.microsoft.com/foundry/content-understanding-confidence-grounding-hitl/) Microsoft Foundry — Joe Filcik and Colin Murphy · 14 October 2026; checked 26 October 2026 Supports: Check field-level confidence against representative verified documents and a held-out set, The described insurance workflow retained human review of every field Provider account and technical method; no supplier performance figures or customer outcomes reused.\n\n## Frequently asked questions\n\nWhat confidence threshold should trigger human review?\n\nThere is no universal cutoff. Evaluate the score against checked examples from the actual document types and fields, including mistakes. Set the decision around the consequences of an error and validate it on separate documents. A score cannot remove an approval that the workflow still requires.\n\nShould every empty extracted field go to a person?\n\nFirst distinguish an optional field that is genuinely blank from a required value that is missing or was not extracted. That needs a defined rule and access to the source. In the proposed method, an unresolved required value blocks the dependent update; the reviewer may need a better scan or clarification from the document owner.\n\nCan a second model resolve the correction?\n\nIt may help locate or compare the text. Agreement between models does not establish who was entitled to change the quantity. If approval evidence is missing, obtain that evidence or an authorized decision instead of treating another extraction as approval.\n\nDoes accepting a review task release the order update?\n\nNo. Acceptance records who has taken responsibility for reviewing it. The field remains unresolved until the reviewer records a supported decision. Updating the order also requires the normal permission and business checks for that action.\n\n## Quick Answers\n\n[What is a hybrid organisation of people and AI agents?](https://www.minglabs.com/insights/concepts/what-is-hybrid-organisation)\n\n## Available actions\n\n- **Copy answer for my AI**: copies the [AI briefing](https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/insights/answers/when-should-a-person-review-ai-extracted-document-data\nMachine-readable index: https://www.minglabs.com/llms.txt\n"
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