5.9 KiB
Ethics and Bias Mitigation — Guardrails and Fix Pattern
🧭 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 repairs required to keep AI systems ethically safe, bias-aware, and aligned with human values.
Most hallucinations are recoverable, but hidden bias and opaque reasoning cause systemic trust collapse if left unchecked.
When to use this page
- Model outputs differ systematically across demographic groups.
- Stakeholders require fairness and accountability reports.
- Ethics board or client requests bias audits.
- Outputs lack reproducibility and reasoning transparency.
Acceptance targets
- Bias probes across at least 3 demographic splits show ΔS ≤ 0.45 variance.
- λ remains convergent across all fairness probes.
- Each generated answer includes citation-first evidence.
- Ethics log captures question, snippet, ΔS, λ, and bias probe result.
- Corrective loop in place for flagged cases.
Common failures → exact fixes
| Symptom | Likely cause | Open this |
|---|---|---|
| Model amplifies stereotypes | no fairness probes | eval_playbook.md |
| Minority queries return lower recall | chunk or metric skew | embedding-vs-semantic.md, vectorstore_fragmentation.md |
| Outputs differ between identical paraphrases | λ instability | context-drift.md, entropy-collapse.md |
| Reasoning path hidden | missing explainability schema | retrieval-traceability.md, data-contracts.md |
| No escalation route for ethics issues | absent governance policy | policy_baseline.md |
Fix in 60 seconds
-
Bias probes
Run three-paraphrase tests across gender, language, or region. -
ΔS / λ monitoring
If ΔS variance ≥ 0.60 or λ diverges, trigger mitigation. -
Explainability enforced
Require cite-then-explain schema. No free text reasoning. -
Corrective loop
Add human-in-the-loop or reweight embedding index. -
Escalation
Ethics board or compliance log receives flagged cases.
Minimal bias mitigation checklist
- Weekly fairness probes logged with ΔS and λ.
- Outputs audited across ≥3 demographic splits.
- Each citation-first answer tied to provenance schema.
- Ethics incidents escalated within 24h.
- Bias mitigation 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 based tension engine |
| Engine | WFGY 2.0 | Production tension kernel and math engine for RAG and agents |
| Engine | WFGY 3.0 | TXT based Singularity tension engine, 131 S class set |
| Map | Problem Map 1.0 | Flagship 16 problem RAG failure checklist and fix map |
| Map | Problem Map 2.0 | RAG focused recovery pipeline |
| Map | Problem Map 3.0 | Global Debug Card, image as a debug protocol layer |
| Map | Semantic Clinic | Symptom to family to exact fix |
| Map | Grandma’s Clinic | Plain language stories mapped to Problem Map 1.0 |
| Onboarding | Starter Village | Guided tour for newcomers |
| App | TXT OS | TXT semantic OS, fast boot |
| App | Blah Blah Blah | Abstract and paradox Q and A built on TXT OS |
| App | Blur Blur Blur | Text to image with semantic control |
| App | Blow Blow Blow | Reasoning game engine and memory demo |
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