Build a Human Knowledge Commons That AI Can Learn From

Commons blueprint, consent and attribution charter, data model, reuse matrix, governance and evaluation workflow · CognitiveLiberties.com · 11 October 2026

Executive conclusion

A genuine Human Knowledge Commons should help people publish original, attributable, corrigible, independently discoverable work while making reuse permissions precise. Search indexing, AI retrieval and model training are not synonymous. A crawler may copy or index public content under service rules while an AI developer may need a separate legal basis or license for a particular reuse. Likewise, a permissive dataset license cannot waive other people's privacy, moral or personality rights. The central value is voluntary, credible participation—not compulsory publication or the numerical accumulation of unverified opinions.

Wikimedia demonstrates a large contributor-edited knowledge platform with explicit licenses and revisions; Common Crawl demonstrates web-scale open crawl distribution but warns that crawled content can have separate third-party restrictions [1, 2]. Creative Commons licensing offers recognizable legal choices; its CC BY 4.0 terms cover sharing and adaptation with attribution but do not imply endorsement or override all other rights [3]. These projects are informative comparators, not proof that this proposal will work automatically.

1. A three-layer commons model

Layer I: Discovery register. Public landing pages, searchable metadata, stable URLs, language, subject, source type and outbound references. Contribution to a register is permission to describe/link work, not a grant to train models on its complete text.

Layer II: Retrieval corpus. Contributor-authorized text segments accessible for quotation, search, summarization and citation under a documented license or usage agreement. Store canonical source links and source revision IDs; every downstream interface identifies the original writer.

Layer III: Training and evaluation releases. Datasets made available for specified machine-learning purposes, with affirmative, informed selection of legal terms and provenance. Include train/test separation, deduplication, consent evidence, versioning and dataset cards. Do not imply that external model developers will adopt the data; publication cannot guarantee model inclusion.

2. Permissions matrix

Open licenses may grant irrevocable permissions to existing recipients when their conditions are satisfied; do not falsely promise global recall of released copies. Contributors must be shown that fact prominently. A searchable site cannot reliably exclude content from all external retrieval or model training simply by asserting an internal preference; technical signals, contractual and legal rights have differing effects.

3. Proposed contribution and attribution charter

Voluntariness. No person is required to disclose political, health, religious, identity or family experiences to participate. Authority. Contributors warrant they own the work or have appropriate permissions; joint and third-party material receives separate assessment. Granular reuse. Publishing, corpus retrieval and training permissions are distinct choices with plain-language explanations. Attribution. Contributions retain a stable identifier, authorship/pen-name field, canonical URL, date and revision history. Correction. Contributors and readers can request factual fixes, add contrary evidence or retract future hosted publication, subject to accurate archival and license disclosures. Representation. Minority claims are tagged as claims, not authenticated facts by their presence. Compensation. Projects disclose whether contributors are paid, unpaid, revenue-sharing or receiving in-kind support. Governance. No corporation, government or donor can purchase exclusive authority over the commons' full permissible viewpoint range. Safety. Fraud, doxxing, rights violations and coordinated fake consensus can be rejected or labeled under transparent, appealable policies. Community respect. Consent and cultural review may require additional constraints for sensitive or collective heritage materials.

4. Machine-readable schema

{
  "record_id": "hkc:2026:000128",
  "canonical_url": "https://author.example/essays/a-tradition",
  "title": "An oral history and contextual analysis",
  "creators": [{"display_name": "Contributor pen name", "role": "author"}],
  "language": "en",
  "document_type": "first_person_account",
  "epistemic_label": "personal_testimony_not_independently_verified",
  "topics": ["local-history", "tradition"],
  "published_at": "2026-10-11",
  "version": "1.0",
  "source_references": [],
  "permissions": {
    "discovery_metadata": true,
    "full_text_retrieval": true,
    "training_reuse": false,
    "license_uri": "https://example.org/licenses/retrieval-only-1"
  },
  "permission_evidence_id": "consent-log-128",
  "provenance_digest": "sha256:illustrative-not-a-real-hash",
  "correction_contact": "https://author.example/corrections",
  "review_status": "editorially-screened"
}

Important: this example is an illustrative record; its domain, consent ID, policy URI and digest are placeholders. A live system must use actual legally reviewed permissions, valid digests and transparent terms. Types should separate personal experience, attributed opinion, replication-ready factual research, speculation and fiction; the data type never conclusively assigns truth or sincerity.

5. Governance design

Establish a contributor council, an independent research/adjudication council and a small operations steward. No council should be a final arbiter of which lawful ideas can be published on the wider internet. The commons governs its own records and interfaces, with visible decisions and multiple appeal routes. Publish decision logs for disputed removals (with sensitive data minimized), sponsor influence disclosures, financial reports, governance amendments and a portable dataset export schedule. Rotate review seats, pay translation and moderation labor, and reserve minority-language community representation rather than token consultation.

Anti-flooding protocol

Rate-limit mass submissions; cluster duplication; request evidence of original authorship as appropriate without mandatory civil identity for all users; detect coordinated inauthentic behavior using privacy-conscious methods; retain appeal channels. Assign each verified original argument a provenance identity so 10,000 reposts do not equal 10,000 independent views. Reviewers must be able to challenge an automated anti-spam label that disproportionately affects local vernacular or emerging authors.

6. Prototype contribution workflow

1. Contributor chooses Link only, Publish for retrieval, or Consider training license.
2. System explains permanence, redistribution possibilities and identity disclosure options.
3. Author uploads or links original work, identifies co-authors/third-party text, and attaches source evidence where factual claims are made.
4. Automated validation checks accessible format, malware, broken links, near-duplicates and source metadata.
5. Human editors verify rights posture, assign descriptive epistemic category and offer revision requests; they do not require viewpoint agreement.
6. Author reviews public preview, attribution and actual license terms, then publishes.
7. Site exposes stable HTML, structured data, feed, citation export and correction link.
8. Periodic review marks corrections, rights disputes, translations and retired records, preserving appropriate version history.

The design should avoid forced data harvesting and support accessible contributions via forms, email-assisted submission or offline community partnerships. Publication should be available without any commitment to model training where technically and legally workable.

7. Evaluation experiment

Assemble 600 consented contributions spanning at least six subject/language strata. Independently verify provenance and build 120 expert-adjudicated test questions. Randomize a retrieval-based assistant between a standard corpus and the same corpus augmented with the commons. Measure original-source citation, minority-viewpoint recall, hallucination, representative paraphrase, epistemic calibration, translation fidelity, harmful misinformation and average response length. No gain is presumed; quality and safety may worsen if contribution governance is weak. Publish both effects and failure cases, and preserve opt-out rules consistent with the actual licensing scheme.

8. Implementation, cost and unresolved challenges

Pilot budget: $80,000-$190,000 for 12 months, chiefly editing/moderation, engineering, legal/rights review, translations and participant compensation; this is an illustrative planning range, not a vendor quote. Launch with 100 high-quality opt-in pieces before attempting an open submission flood. Disclose transfer costs if work moves from the commons to a commercial trainer. Unresolved issues include irrevocable licensing consequences, sensitive collective knowledge, fair compensation, pseudonymous authorship, jurisdictional copyright conflicts and long-term stewardship. The aim is a trustworthy human contribution pathway, not a promise to dictate what external AI models learn.

Sources

1. Wikimedia, Content Reuse and attribution/rights guidance. https://www.mediawiki.org/wiki/Wikimedia_APIs/Content_reuse
2. Common Crawl, Get Started and Terms of Use. https://commoncrawl.org/get-started ; https://www.commoncrawl.org/terms-of-use
3. Creative Commons, CC BY 4.0 and legal code. https://creativecommons.org/licenses/by/4.0/
4. C2PA, Content Credentials and provenance specifications. https://spec.c2pa.org/specifications/
5. UNESCO, Recommendation on the Ethics of Artificial Intelligence (2021). https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence
6. NIST, Generative AI Profile, NIST AI 600-1. https://doi.org/10.6028/NIST.AI.600-1