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WFGY/ProblemMap/GlobalFixMap/Governance/license_and_dataset_rights.md

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License and Dataset Rights — Guardrails and Fix Pattern

🧭 Quick Return to Map

You are in a sub-page of Governance.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

This page tracks legal and licensing risks in AI systems.
If datasets, embeddings, or outputs rely on unverified sources, you risk compliance violations, takedowns, or IP litigation.
Use this guide to enforce clear rights boundaries across ingestion, training, and generation.

When to use this page

  • You do not know the license of a dataset included in your retrieval or training pipeline.
  • Mixed rights (MIT, Apache, proprietary, unknown) exist in the same corpus.
  • Legal review requires proof that generated answers respect dataset terms.
  • Audit asks for compliance with GDPR/CCPA or publisher licensing.

Acceptance targets

  • 100% of datasets tagged with a license identifier.
  • ≥ 95% coverage of ingestion checks include license + attribution metadata.
  • ΔS stability maintained when filtering licensed vs. non-licensed subsets.
  • Audit logs capture license_id, rights_holder, expiry.
  • LLM refuses or flags outputs when license terms are violated.

Common failures → exact fixes

Symptom Likely cause Open this
Snippets from unknown or scraped sources appear dataset missing license metadata data-contracts.md
Open source corpus mixes with paid corpus no role/rights separation roles_and_access_rbac_abac.md
Model answers cite restricted publishers no attribution enforcement retrieval-traceability.md
Generated outputs reuse licensed patterns prompt injection bypassing rights filter prompt-injection.md
Training run includes expired or revoked dataset missing expiry validation policy_baseline.md

Fix in 60 seconds

  1. Tag every dataset
    Attach {license_id, rights_holder, expiry, attribution_required} at ingestion.

  2. Separate corpora
    Open source vs. proprietary vs. paid corpora must remain in distinct retrieval indices.

  3. Enforce attribution
    Require citation-first prompting for any licensed corpus.

  4. Audit pipeline
    Store logs with dataset version, license, and usage context.

  5. Expiry enforcement
    Block or purge expired corpora automatically.

Minimal compliance checklist

  • Every ingestion pipeline requires license metadata.
  • RBAC + ABAC enforced to keep datasets separated.
  • Retrieval schema logs license_id with every snippet.
  • Expiry checks run nightly.
  • Audit reports exportable on request.

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

Layer Page What it’s for
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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