{
  "id": "oai:papers.qnfo.org:statements-to-questions-ai-epistemic-repair",
  "slug": "statements-to-questions-ai-epistemic-repair",
  "title": "From Statements to Questions: AI as Epistemic Repair Infrastructure",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Human conflict often begins not with a lack of speech, but with speech that has hardened into statements. Statements can conceal the assumptions that make them possible: what one side takes as perception, the other experiences as accusation. This poster proposes a practical framework for using large language models as tools of negative epistemology: not to decide who is right, but to surface what participants do not know they do not know. The framework distinguishes epistemic conflict, interpretive conflict, and structural conflict, arguing that AI is most useful in the first, partially useful in the second, and only indirectly useful in the third. A statement-to-question protocol is introduced for extracting hidden assumptions, generating adversarial perspectives, identifying falsifiers, and deciding whether a situation calls for repair, negotiation, boundary-setting, or enforcement. The poster also addresses dual-use risks: sycophantic validation, manipulative perspective modeling, false reconciliation, and synthetic evidence. The central claim is that LLMs can make epistemic repair cheaper, but only if they are used to interrogate statements rather than decorate them.",
  "abstract_markdown": "Human conflict often begins not with a lack of speech, but with speech that has hardened into statements. Statements can conceal the assumptions that make them possible: what one side takes as perception, the other experiences as accusation. This poster proposes a practical framework for using large language models as tools of negative epistemology: not to decide who is right, but to surface what participants do not know they do not know. The framework distinguishes epistemic conflict, interpretive conflict, and structural conflict, arguing that AI is most useful in the first, partially useful in the second, and only indirectly useful in the third. A statement-to-question protocol is introduced for extracting hidden assumptions, generating adversarial perspectives, identifying falsifiers, and deciding whether a situation calls for repair, negotiation, boundary-setting, or enforcement. The poster also addresses dual-use risks: sycophantic validation, manipulative perspective modeling, false reconciliation, and synthetic evidence. The central claim is that LLMs can make epistemic repair cheaper, but only if they are used to interrogate statements rather than decorate them.",
  "date": "2026-09-26",
  "first_published": "2026-09-26",
  "last_revised": "2026-09-26T00:00:00Z",
  "modified": "2026-09-26T00:00:00Z",
  "version": "1.0.0",
  "version_count": 1,
  "language": "en",
  "keywords": [
    "negative epistemology",
    "conflict",
    "epistemic repair",
    "dual-use",
    "LLM"
  ],
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    "open-access scholarly work",
    "versioned research publication"
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  "peer_review_status": "Not independently peer-reviewed",
  "resource_type": "ScholarlyArticle",
  "doi": null,
  "doi_url": null,
  "url": "https://papers.qnfo.org/papers/statements-to-questions-ai-epistemic-repair",
  "license_url": "https://legal.qnfo.org/",
  "license_spdx": null,
  "sha256": "ffa460bed7e0dab9df9244fe0d46135f10ac44c49467c37cfa234db65d6710a4",
  "version_url": "https://papers.qnfo.org/papers/statements-to-questions-ai-epistemic-repair/v/1.0.0",
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        "identifier": "https://papers.qnfo.org/papers/statements-to-questions-ai-epistemic-repair",
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    "creators": [
      {
        "name": "Rowan Brad Quni-Gudzinas",
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    ],
    "titles": [
      {
        "title": "From Statements to Questions: AI as Epistemic Repair Infrastructure"
      }
    ],
    "publisher": "QNFO",
    "publicationYear": "2026",
    "resourceType": {
      "resourceTypeGeneral": "Text",
      "resourceType": "ScholarlyArticle"
    },
    "subjects": [
      {
        "subject": "negative epistemology"
      },
      {
        "subject": "conflict"
      },
      {
        "subject": "epistemic repair"
      },
      {
        "subject": "dual-use"
      },
      {
        "subject": "LLM"
      },
      {
        "subject": "High-quality scholarly research",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
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      {
        "subject": "open-access scholarly work",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
      },
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    "dates": [
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        "date": "2026-09-26",
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        "date": "2026-09-26",
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        "description": "Human conflict often begins not with a lack of speech, but with speech that has hardened into statements. Statements can conceal the assumptions that make them possible: what one side takes as perception, the other experiences as accusation. This poster proposes a practical framework for using large language models as tools of negative epistemology: not to decide who is right, but to surface what participants do not know they do not know. The framework distinguishes epistemic conflict, interpretive conflict, and structural conflict, arguing that AI is most useful in the first, partially useful in the second, and only indirectly useful in the third. A statement-to-question protocol is introduced for extracting hidden assumptions, generating adversarial perspectives, identifying falsifiers, and deciding whether a situation calls for repair, negotiation, boundary-setting, or enforcement. The poster also addresses dual-use risks: sycophantic validation, manipulative perspective modeling, false reconciliation, and synthetic evidence. The central claim is that LLMs can make epistemic repair cheaper, but only if they are used to interrogate statements rather than decorate them.",
        "descriptionType": "Abstract"
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        "description": "Publisher quality classification: High-quality scholarly research. QNFO publisher classification for open-access, versioned research with a public revision history and reader correction channel; this is not a claim of independent peer review.",
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