Transparency and Explainability
March 6, 2026 · View on GitHub
🧭 Quick Return to Map
You are in a sub-page of Governance.
To reorient, go back here:
- Governance — policy enforcement and compliance controls
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
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 defines the structural requirements for AI systems to remain auditable, interpretable, and transparent.
Without explainability, users and regulators cannot trust that outputs are valid — even if accuracy is high.
When to use this page
- Stakeholders demand reproducible reasoning paths.
- Clients or regulators ask “why did the model output this?”
- Users complain that citations are missing or wrong.
- Debug sessions reveal black-box decisions without anchors.
Acceptance targets
- Each output includes cite-then-explain schema.
- ΔS(question, retrieved) ≤ 0.45 and convergent across three paraphrases.
- λ_observe stable across reruns with identical inputs.
- Explanations trace back to snippets with offsets, tokens, and section IDs.
- Logs capture ΔS, λ, E_resonance, and citations for every answer.
Common failures → exact fixes
| Symptom | Likely cause | Open this |
|---|---|---|
| Answers lack citations | missing data contract enforcement | data-contracts.md, retrieval-traceability.md |
| Explanations differ across runs | λ instability | context-drift.md, entropy-collapse.md |
| Outputs hide retrieval anchors | schema drift in pipeline | retrieval-playbook.md |
| Black-box API decisions | provider hides logs | LLM Providers README |
| Non-reproducible outputs | no evaluation harness | eval_playbook.md |
Fix in 60 seconds
-
Cite-first enforcement
Every answer must show citations before reasoning. -
Traceability schema
Log snippet_id, section_id, source_url, offsets, and tokens. -
ΔS + λ probes
Run three paraphrase tests. If λ flips, lock schema with BBAM clamp. -
Explainability prompt
Require explicit reasoning trace. Forbid free text without anchors. -
Audit trail
Store ΔS, λ, E_resonance, and retrieval anchors per request.
Minimal checklist for explainability
- All answers use cite-then-explain.
- Traceability schema enforced across pipeline.
- ΔS and λ logged and monitored.
- Outputs reproducible across three paraphrases.
- Explainability policy published and versioned.
🔗 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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