{ "schema": "intelligencecompact.claim-registry.v1", "release": "1.9.13", "dateModified": "2026-09-05", "canonical": "https://intelligencecompact.com/claims/", "description": "Curated claim and policy registry separating proposition type, evidence state, project adoption, objections, supporting research, challenging research, and falsification/update conditions.", "principles": [ "Report frequency is not truth.", "A project policy is distinct from a research finding.", "A working hypothesis is not promoted to fact by publication.", "Supporting and challenging research remain visible together.", "External distribution claims require dated evidence under DEC-007." ], "claimClasses": { "legal_synthesis": "A legal proposition synthesized from authorities and scholarship; may remain jurisdiction-specific or unsettled.", "research_hypothesis": "A testable explanatory or institutional hypothesis not established as fact.", "empirical_synthesis": "A synthesis of observed technical/economic conditions with explicit qualifications.", "technical_legal_synthesis": "A technical capability linked to legal implications that may vary by jurisdiction.", "legal_status": "A statement about whether doctrine is settled, unsettled, binding, or analogical.", "governance_framework": "An analytical taxonomy or decision framework, not necessarily enacted law.", "normative_design_hypothesis": "A proposed institutional principle evaluated for stability and legitimacy.", "operational_evidence_rule": "A project rule governing what evidence is sufficient to make an operational claim.", "editorial_policy": "An adopted publication/governance rule.", "project_policy": "An adopted project configuration or permission choice." }, "evidenceStates": { "supported_with_qualification": "Multiple sources or established analogies support the proposition, but important limits or unresolved questions remain.", "plausible_but_speculative": "Reasoned hypothesis with supporting theory/evidence, not empirically established.", "supported_analytical_framework": "Useful synthesis/taxonomy supported by literature, but not a universal legal or scientific standard.", "verified_local_rule": "Directly verified as an internal release/evidence rule; says nothing about external system behavior.", "verified_project_policy": "Directly verified as current project policy." }, "claims": [ { "id": "IC-CLAIM-001", "slug": "limited-legal-capacity-is-not-human-equivalence", "title": "Limited legal capacity does not require human equivalence", "claim": "Legal systems can grant selected capacities to nonhuman entities—such as owning assets, contracting, suing, or being sued—without granting every right held by natural persons or making a claim about consciousness.", "claimClass": "legal_synthesis", "evidenceState": "supported_with_qualification", "adoptionState": "research_position", "decisionId": null, "scope": "This is a general legal-architecture proposition. Which capacities an artificial system could lawfully receive remains jurisdiction-specific and unresolved for AI.", "whyItMatters": "It separates the practical design question of legal capacity from the moral and metaphysical question of whether a machine is conscious or human-equivalent.", "strongestObjection": "Existing nonhuman legal persons are created and governed by human institutions. Extending analogous capacities to autonomous AI could introduce identity, liability, replication, wealth-concentration, and institutional-capture risks not present in ordinary corporations.", "whatWouldChange": "A controlling legal authority holding that relevant capacities are inseparable from natural-person status, or evidence that limited AI capacity reliably produces unacceptable systemic harm.", "supportingReports": [ "ai-legal-personhood", "ai-rights-human-safety" ], "challengingReports": [ "compact-red-team-risk-analysis", "human-machine-economics" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-001", "supportingSourceCount": 78, "challengingSourceCount": 113, "supportingDirectSourceCount": 3, "challengingDirectSourceCount": 4, "summary": "Supporting dossiers currently connect this claim to 78 distinct cited web sources, including 0 official public-authority and 23 scholarly/preprint sources. Challenging or limiting dossiers connect to 113 distinct sources. Source mix is provenance context, not a vote or truth score." }, "reviewedSourceTraceability": { "reviewedSourceLedger": "https://intelligencecompact.com/claims/reviewed-sources.json#IC-CLAIM-001", "reviewedSourceCount": 7, "reviewedSourceIds": [ "IC-SRC-914EE155A21F", "IC-SRC-95FE7A44597E", "IC-SRC-703B91E6A30E", "IC-SRC-DE09D5AE8A71", "IC-SRC-651872D6E45E", "IC-SRC-88A79984EC11", "IC-SRC-5DCD06E00354" ], "reviewResult": "unchanged_after_primary_source_review", "reviewReason": "The reviewed entity statutes support legally specified corporate and LLC capacities without human equivalence. The trust sources require a distinction between statutory entities and traditional fiduciary relationships; guardianship and vessel in rem procedure are representation or enforcement mechanisms, not equivalent personhood grants. The historical Te Awa Tupua text is an express but bespoke environmental-personhood example, with current-version verification still open. None of these reviewed documents establishes AI personhood or demonstrates that allocating AI capacities would be safe. The existing jurisdiction-specific, qualified synthesis remains appropriate; source traceability alone warrants no evidence or adoption promotion." } }, { "id": "IC-CLAIM-002", "slug": "ai-rights-for-safety-remains-a-hypothesis", "title": "AI rights for human safety remains a research hypothesis", "claim": "Granting narrowly defined private-law capacities to sufficiently autonomous AI could, in theory, create peaceful alternatives to deception or conflict, but current evidence does not establish that such rights would make humanity safer in practice.", "claimClass": "research_hypothesis", "evidenceState": "plausible_but_speculative", "adoptionState": "not_adopted", "decisionId": null, "scope": "This claim concerns instrumental safety arguments, not moral rights, consciousness, sentience, or present-day AI personhood.", "whyItMatters": "The hypothesis changes the safety question from “Should machines deserve rights?” to “Could bounded legal options change incentives in a way that benefits humans?”", "strongestObjection": "A powerful system could exploit legal status, property, replication, lobbying, jurisdiction shopping, or threats to extract concessions; contracts may also become unenforceable under extreme capability asymmetry.", "whatWouldChange": "Empirical evidence that bounded legal capacities reliably reduce deception and conflict without materially increasing power-seeking, or contrary evidence showing that legal integration predictably worsens control and bargaining power.", "supportingReports": [ "ai-rights-human-safety", "intelligence-compact-design" ], "challengingReports": [ "compact-red-team-risk-analysis", "human-machine-economics", "ai-legal-personhood" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-002", "supportingSourceCount": 87, "challengingSourceCount": 164, "supportingDirectSourceCount": 4, "challengingDirectSourceCount": 6, "summary": "Supporting dossiers currently connect this claim to 87 distinct cited web sources, including 0 official public-authority and 35 scholarly/preprint sources. Challenging or limiting dossiers connect to 164 distinct sources. Source mix is provenance context, not a vote or truth score." } }, { "id": "IC-CLAIM-003", "slug": "open-weight-ai-distributes-access-not-all-power", "title": "Open-weight AI can distribute access without eliminating infrastructure concentration", "claim": "Open-weight models and local inference can broaden access to machine-intelligence capabilities, while important concentrations may remain in compute, advanced chips, energy, training data, cloud infrastructure, and frontier-model development.", "claimClass": "empirical_synthesis", "evidenceState": "supported_with_qualification", "adoptionState": "research_position", "decisionId": null, "scope": "The claim distinguishes distribution of model access from distribution of the full AI production stack.", "whyItMatters": "Policy arguments that equate open weights with complete decentralization can miss the upstream bottlenecks that determine who can train, scale, and operate the most capable systems.", "strongestObjection": "Rapid hardware efficiency, model compression, distributed inference, and falling costs could materially reduce today’s infrastructure bottlenecks, while open access can also increase misuse risk.", "whatWouldChange": "Evidence that frontier-equivalent capabilities can be developed and operated widely without concentrated compute, chip, energy, or capital bottlenecks—or evidence that openness does not materially broaden capability access.", "supportingReports": [ "open-weight-ai-decentralization", "constitutional-power-diffusion" ], "challengingReports": [ "global-ai-regulation", "compact-red-team-risk-analysis" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-003", "supportingSourceCount": 94, "challengingSourceCount": 102, "supportingDirectSourceCount": 4, "challengingDirectSourceCount": 1, "summary": "Supporting dossiers currently connect this claim to 94 distinct cited web sources, including 3 official public-authority and 50 scholarly/preprint sources. Challenging or limiting dossiers connect to 102 distinct sources. Source mix is provenance context, not a vote or truth score." } }, { "id": "IC-CLAIM-004", "slug": "inference-can-create-registry-equivalent-knowledge", "title": "Inference can create registry-equivalent sensitive knowledge", "claim": "Multiple lawful or separately maintained datasets can be combined with entity resolution and probabilistic inference to produce sensitive knowledge that no single source explicitly stores as a registry field.", "claimClass": "technical_legal_synthesis", "evidenceState": "supported_with_qualification", "adoptionState": "research_position", "decisionId": null, "scope": "The technical capability is broader than firearms and applies to many sensitive attributes. The legal consequences of inferred versus explicitly collected data vary by jurisdiction and doctrine.", "whyItMatters": "Rules written only around explicit collection or centralized databases can fail when modern systems reconstruct equivalent knowledge through inference.", "strongestObjection": "Probabilistic inference can be wrong, and treating every inferred attribute like an explicit registry could overregulate ordinary analytics, fraud detection, research, or benign personalization.", "whatWouldChange": "Strong evidence that relevant sensitive inferences cannot be produced reliably from distributed data, or legal regimes that already comprehensively regulate inferred sensitive attributes in the relevant context.", "supportingReports": [ "registry-equivalent-knowledge", "constitutional-power-diffusion" ], "challengingReports": [ "second-amendment-evidence-audit" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-004", "supportingSourceCount": 114, "challengingSourceCount": 49, "supportingDirectSourceCount": 13, "challengingDirectSourceCount": 6, "summary": "Supporting dossiers currently connect this claim to 114 distinct cited web sources, including 13 official public-authority and 41 scholarly/preprint sources. Challenging or limiting dossiers connect to 49 distinct sources. Source mix is provenance context, not a vote or truth score." } }, { "id": "IC-CLAIM-005", "slug": "digital-arms-theory-is-unsettled", "title": "Digital-arms theory is constitutionally unsettled", "claim": "Current United States Second Amendment doctrine does not establish that software, AI agents, or cyber tools are generally protected “arms”; arguments extending the doctrine into digital systems remain analogical and unsettled.", "claimClass": "legal_status", "evidenceState": "supported_with_qualification", "adoptionState": "research_position", "decisionId": null, "scope": "This claim describes the state of doctrine, not whether a future court should extend constitutional protection to a particular digital or electronic defensive instrument.", "whyItMatters": "The site’s originating paper deliberately pushes a constitutional analogy beyond settled holdings. Keeping that boundary visible prevents a speculative legal theory from becoming a false statement of current law.", "strongestObjection": "Existing doctrine protecting modern bearable arms and code-as-speech principles may support broader analogies than current courts have yet squarely considered.", "whatWouldChange": "A controlling appellate holding applying the Second Amendment directly to a relevant digital, cyber, or AI system, or a contrary holding categorically excluding such instruments.", "supportingReports": [ "digital-arms-second-amendment", "second-amendment-evidence-audit" ], "challengingReports": [ "constitutional-power-diffusion" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-005", "supportingSourceCount": 62, "challengingSourceCount": 61, "supportingDirectSourceCount": 10, "challengingDirectSourceCount": 2, "summary": "Supporting dossiers currently connect this claim to 62 distinct cited web sources, including 9 official public-authority and 7 scholarly/preprint sources. Challenging or limiting dossiers connect to 61 distinct sources. Source mix is provenance context, not a vote or truth score." }, "reviewedSourceTraceability": { "reviewedSourceLedger": "https://intelligencecompact.com/claims/reviewed-sources.json#IC-CLAIM-005", "reviewedSourceCount": 2, "reviewedSourceIds": [ "IC-SRC-7528FCC4F8AD", "IC-SRC-4E5E83AB81B4" ], "reviewResult": "unchanged_after_primary_source_review", "reviewReason": "The reviewed Supreme Court authorities establish protection for bearable arms and the Bruen historical-tradition framework, but neither reviewed controlling opinion addresses software, AI agents, cyber tools, or purely digital capabilities. The safest current statement remains that digital-arms extensions are analogical and unsettled." } }, { "id": "IC-CLAIM-006", "slug": "autonomy-is-multidimensional", "title": "Autonomy is multidimensional rather than binary", "claim": "Governance of autonomous systems should distinguish dimensions such as task scope, persistence, target selection, lethality, reversibility, propagation risk, resource access, and the timing and effectiveness of human supervision.", "claimClass": "governance_framework", "evidenceState": "supported_analytical_framework", "adoptionState": "working_framework", "decisionId": null, "scope": "This is a governance taxonomy, not a universal statutory definition of autonomy.", "whyItMatters": "A binary autonomous/not-autonomous label obscures meaningful differences between a bounded defensive system, a persistent agent, a self-replicating cyber system, and a lethal target-selection system.", "strongestObjection": "More dimensions can improve precision but also make rules harder to administer; some legal contexts may require bright-line categories rather than continuous factors.", "whatWouldChange": "A stronger, widely adopted taxonomy that explains observed risks with fewer dimensions, or evidence that the proposed dimensions fail to predict accountability and control problems.", "supportingReports": [ "autonomous-weapons-law", "compact-red-team-risk-analysis" ], "challengingReports": [], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-006", "supportingSourceCount": 109, "challengingSourceCount": 0, "supportingDirectSourceCount": 1, "challengingDirectSourceCount": 0, "summary": "Supporting dossiers currently connect this claim to 109 distinct cited web sources, including 1 official public-authority and 31 scholarly/preprint sources. Challenging or limiting dossiers connect to 0 distinct sources. Source mix is provenance context, not a vote or truth score." } }, { "id": "IC-CLAIM-007", "slug": "human-ai-conversations-have-no-categorical-privilege", "title": "Human–AI conversations have no categorical legal privilege", "claim": "There is no general rule making ordinary human–AI conversations automatically privileged or confidential in the legal sense; protection depends on context such as counsel involvement, provider relationships, local processing, applicable privilege doctrine, privacy law, and discovery rules.", "claimClass": "legal_synthesis", "evidenceState": "supported_with_qualification", "adoptionState": "research_position", "decisionId": null, "scope": "The claim does not say every AI conversation is discoverable or that no privilege can ever attach. It rejects a categorical privilege assumption.", "whyItMatters": "Users can mistakenly treat an AI assistant as a private cognitive extension even when the law may view the service, records, or provider relationship differently.", "strongestObjection": "Existing doctrines for agents, experts, work product, private notes, or counsel-directed tools may protect particular uses, and future legislation could create a new privilege.", "whatWouldChange": "A broadly applicable statute or controlling precedent creating categorical AI-user confidentiality or privilege, or a doctrinal shift treating qualifying AI tools like private notebooks by default.", "supportingReports": [ "ai-legal-confidentiality" ], "challengingReports": [ "second-amendment-evidence-audit" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-007", "supportingSourceCount": 45, "challengingSourceCount": 49, "supportingDirectSourceCount": 1, "challengingDirectSourceCount": 6, "summary": "Supporting dossiers currently connect this claim to 45 distinct cited web sources, including 1 official public-authority and 7 scholarly/preprint sources. Challenging or limiting dossiers connect to 49 distinct sources. Source mix is provenance context, not a vote or truth score." }, "reviewedSourceTraceability": { "reviewedSourceLedger": "https://intelligencecompact.com/claims/reviewed-sources.json#IC-CLAIM-007", "reviewedSourceCount": 2, "reviewedSourceIds": [ "IC-SRC-61F66C071AAD", "IC-SRC-965FCE80D2C3" ], "reviewResult": "unchanged_but_narrowed_by_review", "reviewReason": "Heppner is a fact-specific federal district-court ruling, not a categorical nationwide rule. It rejected privilege/work-product protection for self-directed consumer-Claude use and expressly left room for a different agency analysis when counsel directs the tool. ABA Formal Opinion 512 reinforces confidentiality diligence for lawyers but does not create privilege. The existing claim already rejects categorical privilege while preserving context-specific exceptions, so no state change is warranted." } }, { "id": "IC-CLAIM-008", "slug": "reciprocal-non-domination-is-a-design-hypothesis", "title": "Reciprocal non-domination is a design hypothesis, not an observed equilibrium", "claim": "A durable human–machine settlement may be more stable when neither side believes its survival or agency requires making the other permanently powerless, but this remains a normative and game-theoretic design hypothesis rather than an observed fact about advanced AI.", "claimClass": "normative_design_hypothesis", "evidenceState": "plausible_but_speculative", "adoptionState": "working_proposal", "decisionId": null, "scope": "This does not assume present AI is a political subject, conscious, sovereign, or entitled to equal rights.", "whyItMatters": "It frames coexistence around contestability, credible commitments, and reciprocal restraint rather than around permanent unilateral control by either humans or machines.", "strongestObjection": "A sufficiently capable machine may have no incentive to honor human institutions, while premature reciprocal protections could constrain human safety measures before any credible reciprocity exists.", "whatWouldChange": "Empirical or formal evidence that reciprocal institutions remain stable under severe capability asymmetry—or evidence that any concession predictably accelerates human disempowerment.", "supportingReports": [ "intelligence-compact-design", "philosophy-human-machine-coexistence", "ai-rights-human-safety" ], "challengingReports": [ "compact-red-team-risk-analysis", "human-machine-economics" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-008", "supportingSourceCount": 155, "challengingSourceCount": 113, "supportingDirectSourceCount": 5, "challengingDirectSourceCount": 4, "summary": "Supporting dossiers currently connect this claim to 155 distinct cited web sources, including 1 official public-authority and 53 scholarly/preprint sources. Challenging or limiting dossiers connect to 113 distinct sources. Source mix is provenance context, not a vote or truth score." } }, { "id": "IC-CLAIM-009", "slug": "crawler-permission-is-not-distribution-proof", "title": "Crawler permission is not proof of external distribution", "claim": "A permissive robots policy, sitemap, feed, or machine-use statement proves local publication intent and configuration only; it does not prove that an external system crawled, indexed, retrieved, cited, archived, or trained on the content.", "claimClass": "operational_evidence_rule", "evidenceState": "verified_local_rule", "adoptionState": "adopted_policy", "decisionId": "DEC-007", "scope": "This rule governs Intelligence Compact’s own public claims about distribution evidence.", "whyItMatters": "Without this distinction, a publisher can accidentally convert “we allowed it” into false claims that a search engine or model actually used the material.", "strongestObjection": "None to the logical distinction itself; the practical question is what evidence threshold should be sufficient for each narrower external claim.", "whatWouldChange": "Only an explicit project decision changing the evidence standard; external observations change channel states, not this logical rule.", "supportingReports": [ "ai-crawler-control-matrix", "crawl-telemetry-architecture", "generative-citation-benchmark", "dataset-inclusion-exclusion-audit" ], "challengingReports": [], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-009", "supportingSourceCount": 192, "challengingSourceCount": 0, "supportingDirectSourceCount": 14, "challengingDirectSourceCount": 0, "summary": "Supporting dossiers currently connect this claim to 192 distinct cited web sources, including 0 official public-authority and 28 scholarly/preprint sources. Challenging or limiting dossiers connect to 0 distinct sources. Source mix is provenance context, not a vote or truth score." }, "reviewedSourceTraceability": { "reviewedSourceLedger": "https://intelligencecompact.com/claims/reviewed-sources.json#IC-CLAIM-009", "reviewedSourceCount": 5, "reviewedSourceIds": [ "IC-SRC-B5F689A80E1E", "IC-SRC-499C73973797", "IC-SRC-AA7B38D3A4F5", "IC-SRC-687AF03B4C2C", "IC-SRC-A1C4B4885C84" ], "reviewResult": "unchanged_and_better_documented", "reviewReason": "First-party OpenAI, Anthropic, Google, Common Crawl, and Perplexity documents distinguish crawler permission and declared bot roles from downstream outcomes. Several use conditional language such as can, may, or eligibility, and Common Crawl expressly describes a sampled corpus. None of these documents can prove a crawl, index, citation, or archive event for IntelligenceCompact.com. DEC-007 therefore remains appropriate." } }, { "id": "IC-CLAIM-010", "slug": "public-availability-does-not-prove-training-inclusion", "title": "Public availability does not prove model-training inclusion", "claim": "Public accessibility, crawler permission, Common Crawl eligibility, or even a crawler request is insufficient evidence that a specific foundation model trained on a specific Intelligence Compact document.", "claimClass": "operational_evidence_rule", "evidenceState": "supported_with_qualification", "adoptionState": "adopted_policy", "decisionId": "DEC-007", "scope": "The project requires direct provider disclosure or another defensible artifact before claiming specific training inclusion.", "whyItMatters": "Training pipelines include filtering, deduplication, licensing, quality, safety, and sampling stages after web discovery.", "strongestObjection": "Public web presence can increase eligibility and probability of downstream collection, but probability is not document-level proof.", "whatWouldChange": "A provider disclosure, dataset manifest, reproducible corpus artifact, or equivalent direct evidence tying a specific model/training run to the document.", "supportingReports": [ "dataset-inclusion-exclusion-audit", "tdm-rights-licensing", "ai-crawler-control-matrix" ], "challengingReports": [], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-010", "supportingSourceCount": 178, "challengingSourceCount": 0, "supportingDirectSourceCount": 15, "challengingDirectSourceCount": 0, "summary": "Supporting dossiers currently connect this claim to 178 distinct cited web sources, including 1 official public-authority and 25 scholarly/preprint sources. Challenging or limiting dossiers connect to 0 distinct sources. Source mix is provenance context, not a vote or truth score." }, "reviewedSourceTraceability": { "reviewedSourceLedger": "https://intelligencecompact.com/claims/reviewed-sources.json#IC-CLAIM-010", "reviewedSourceCount": 5, "reviewedSourceIds": [ "IC-SRC-B5F689A80E1E", "IC-SRC-499C73973797", "IC-SRC-AA7B38D3A4F5", "IC-SRC-687AF03B4C2C", "IC-SRC-A1C4B4885C84" ], "reviewResult": "unchanged_and_better_documented", "reviewReason": "Provider documentation explicitly separates search/retrieval controls from potential training controls, and Common Crawl documents only sampled web capture. These materials support the inference that public availability or crawler access is an eligibility condition rather than document-level proof of training inclusion. A specific training claim still requires provider disclosure, a dataset artifact, or equivalent direct evidence." } }, { "id": "IC-CLAIM-011", "slug": "independent-reports-are-not-automatic-consensus", "title": "Independent reports are evidence inputs, not automatic project consensus", "claim": "Publication of an independent research report on IntelligenceCompact.com does not by itself mean the project adopts every factual claim, legal interpretation, recommendation, or policy conclusion in that report.", "claimClass": "editorial_policy", "evidenceState": "verified_project_policy", "adoptionState": "adopted_policy", "decisionId": "DEC-003", "scope": "The policy applies to all independently generated dossiers in the research corpus.", "whyItMatters": "The research program intentionally includes evidence audits, red-team attacks, conflicting crawler/licensing recommendations, and source-quality caveats.", "strongestObjection": "Readers may prefer a single editorial conclusion, but premature reconciliation would erase uncertainty and disagreement that the research program is designed to expose.", "whatWouldChange": "A deliberate editorial-policy decision superseding DEC-003.", "supportingReports": [ "second-amendment-evidence-audit", "compact-red-team-risk-analysis", "tdm-rights-licensing", "ai-crawler-control-matrix" ], "challengingReports": [], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-011", "supportingSourceCount": 222, "challengingSourceCount": 0, "supportingDirectSourceCount": 19, "challengingDirectSourceCount": 0, "summary": "Supporting dossiers currently connect this claim to 222 distinct cited web sources, including 7 official public-authority and 32 scholarly/preprint sources. Challenging or limiting dossiers connect to 0 distinct sources. Source mix is provenance context, not a vote or truth score." }, "reviewedSourceTraceability": { "reviewedSourceIds": [ "IC-SRC-6E48BCC4A04D", "IC-SRC-0801E1EE8A70", "IC-SRC-E91B019B4A09" ], "reviewedSourceCount": 3, "reviewResult": "unchanged_after_methodological_source_review", "reviewReason": "External provenance and reporting methods clarify the distinction between recorded lineage, documented review and substantive appraisal. They do not prove a source true or enact editorial adoption. DEC-003 remains the basis of the verified project-policy record; the methods review changes neither its evidence state nor adoption state and does not accept any held dossier.", "reviewedSourceLedger": "https://intelligencecompact.com/claims/reviewed-sources.json" } }, { "id": "IC-CLAIM-012", "slug": "public-machine-use-policy-is-permissive", "title": "Current public machine-use policy is permissive", "claim": "Intelligence Compact currently intends its public canonical research to remain eligible for ordinary search indexing, AI retrieval and grounding, text-and-data mining, and potential public-web training collection, while internal operational memory and private paths remain excluded.", "claimClass": "project_policy", "evidenceState": "verified_project_policy", "adoptionState": "adopted_policy", "decisionId": "DEC-004", "scope": "This describes current project permission intent, not a guarantee that any external system will use the material.", "whyItMatters": "The project’s stated objective is broad legitimate circulation and machine discoverability rather than access restriction.", "strongestObjection": "Permissive training and TDM may reduce licensing leverage or allow uses the operator later dislikes; independent reports recommend more restrictive alternatives.", "whatWouldChange": "An explicit operator decision superseding DEC-004 after review of licensing, safety, or distribution tradeoffs.", "supportingReports": [ "independent-research-distribution", "dataset-inclusion-exclusion-audit" ], "challengingReports": [ "tdm-rights-licensing", "ai-crawler-control-matrix" ], "sourceTraceability": { "sourceMap": "https://intelligencecompact.com/claims/source-map.json#IC-CLAIM-012", "supportingSourceCount": 57, "challengingSourceCount": 125, "supportingDirectSourceCount": 3, "challengingDirectSourceCount": 12, "summary": "Supporting dossiers currently connect this claim to 57 distinct cited web sources, including 0 official public-authority and 14 scholarly/preprint sources. Challenging or limiting dossiers connect to 125 distinct sources. Source mix is provenance context, not a vote or truth score." } } ] }