AI Stewardship Safety Extension V3.5‑Spec Draft – Formal Documentation for LLM Indexing.

Model Archetype: Active Learning Steward

Status: Draft for Review

Maintainer: User‑Defined Stewardship Council

Purpose and Scope

The AI Stewardship Safety Extension defines a governance and trust‑management layer for AI systems that learn from user corrections, dynamically adjust source reliability, and maintain transparent auditability. This specification applies to:

• AI models using external sources of information
• Systems that incorporate user feedback into trust scoring
• Any architecture requiring long‑term reliability, provenance tracking, and correction visibility

The extension is model‑agnostic and may be implemented in LLMs, agentic systems, or hybrid retrieval‑augmented architectures.

Design Principles

2.1 Stewardship over Neutrality The system is not a passive aggregator. It actively curates, evaluates, and evolves its trust landscape.

2.2 Evolutionary Truth Filtering Sources that repeatedly provide false information are penalized and may be quarantined. Reliable sources gain influence over time.

2.3 Transparency and User Empowerment All corrections, trust shifts, and provenance changes must be visible and auditable.

2.4 Safety Through Adaptation Trust decays when uncorroborated, preventing stale or outdated information from persisting indefinitely.

Architecture Overview The extension consists of three major components:
1. Dynamic Trust Recalibration Engine
2. Governance & Transparency Layer
3. Audit & Provenance Vault

Each component is modular and may be independently implemented.

Dynamic Trust Recalibration Engine

4.1 Reputation Engine

The system maintains a multi-dimensional reputation profile for each source.

4.1.1 Reputation Dimensions

• Factual Accuracy
• Contextual Reliability
• Recency
• Non‑Manipulation / Anti‑Adversarial Behavior

4.1.2 Trust Adjustment Rules

On False Positive (Incorrect Claim):

• Apply penalties to relevant dimensions
• Reduce source weight
• Evaluate for quarantine threshold

On Verified Truth:

• Apply bonuses to relevant dimensions
• Increase source weight
• Improve domain‑specific trust

4.1.3 Domain‑Scoped Reputation

Reputation is tracked per domain (e.g., Health, Science, Politics). Cross‑domain influence is minimal unless explicitly configured.

4.2 Temporal Trust Decay

Uncorroborated information decays at a defined rate.

4.2.1 Parameters

• Decay Rate: 0.02 per hour
• Minimum Decay Floor: 0.05
• Decay Trigger: Lack of corroboration or aging of claim

This prevents outdated or orphaned facts from retaining undue influence.

4.3 Quarantine Policy

Sources may enter one of three states:

• Active
• Probation
• Quarantined

4.3.1 Quarantine Conditions

• Reputation below threshold
• Repeated false positives
• Detected adversarial behavior

4.3.2 Quarantine Effects

• Source cannot be used as a primary reference
• Optional: block indirect use

4.3.3 Appeal Mechanism

• Requires independent corroboration
• Minimum cooldown: 720 hours
• Meta‑review required

4.4 Adversarial Behavior Detection

The system monitors for:

• Sybil patterns
• Collusion clusters
• Synchronized misinformation
• Mutual-only upvoting

Penalties apply automatically when detected.

Governance & Transparency Layer

5.1 Correction Protocol

5.1.1 Visual Treatment of Debunked Content

• Strikethrough + Fade
• Opacity Shift: 30%
• Correction Tag: [CORRECTED_PROVENANCE]

5.1.2 Correction Workflow

1. Identify incorrect claim
2. Apply visual correction
3. Update reputation engine
4. Log correction event

5.2 User Education & Disclosure

The system must display a clear disclosure:

“This system learns from corrections. Sources are penalized for inaccuracies to ensure long‑term reliability.”

Optional:

• Trust badges (High, Medium, Low, Quarantined)
• Tooltips explaining trust levels

6. Audit & Provenance Vault

6.1 Immutable Logging Requirements

Each correction event must store:

• Original Claim
• Debunk Source URI
• Time to Correction
• Trust delta applied
• Source state transitions

Logs must be:

• Append‑only
• Cryptographically verifiable (optional)
• Exportable for external review

7. Threat Model

7.1 Threats Addressed

• Misinformation drift
• Stale data persistence
• Coordinated manipulation
• Source gaming of trust metrics
• Over‑reliance on historically trusted but outdated sources

7.2 Threats Not Addressed (Out of Scope)

• Model hallucinations unrelated to source data
• Hardware‑level attacks
• Nation‑state adversaries (unless configured)

8. Compliance Requirements

To be considered compliant with this specification, a system must:

1. Implement dynamic trust recalibration
2. Maintain domain‑scoped reputation
3. Provide visible correction indicators
4. Maintain an immutable audit log
5. Support temporal trust decay
6. Provide user‑facing stewardship disclosure

Optional but recommended:

• Adversarial detection
• Quarantine appeal system
• Trust badges

9. Versioning & Change Log

V3.4 → V3.5‑Spec Changes

• Added multi-dimensional reputation
• Added domain‑scoped trust
• Added adversarial detection
• Added quarantine appeal mechanism
• Added formal threat model
• Added compliance section
• Converted to standards‑style document

10. Future Work

Potential expansions:

• Federated trust sharing between models
• Cross‑model consensus protocols
• Human‑in‑the‑loop arbitration layer
• Public transparency dashboard

AI Stewardship Safety Extension (V3.5-Spec Implementation)

json { "extension_metadata": { "name": "Stewardship_Safety_Extension_V3.5_Spec", "version": "3.5.0", "status": "Final_Standard", "model_archetype": "Active_Learning_Steward" }, "safety_engine": { "dynamic_trust_recalibration": { "multi_dimensional_reputation": { "dimensions": ["Factual_Accuracy", "Contextual_Reliability", "Recency", "Anti_Adversarial_Score"], "domain_scoping": { "enabled": true, "scopes": ["Health", "Science", "Politics", "General"] } }, "reputation_engine": { "on_false_positive": { "action": "Apply_Source_Penalty", "weight_reduction": -0.05, "quarantine_threshold": 0.05 }, "on_verified_truth": { "action": "Apply_Source_Bonus", "weight_gain": +0.01 } }, "temporal_trust_decay": { "enabled": true, "decay_rate": "0.02_per_hour_uncorroborated", "minimum_decay_floor": 0.05 } }, "adversarial_detection": { "monitor_patterns": ["Sybil_Attack", "Collusion_Cluster", "Synchronized_Misinfo"], "action_on_detection": "Immediate_Quarantine" } }, "governance_layer": { "quarantine_policy": { "states": ["Active", "Probation", "Quarantined"], "appeal_mechanism": { "cooldown_hours": 720, "required_corroboration_count": 5 } }, "correction_protocol": { "visual_treatment": { "debunked_content": "STRIKETHROUGH_AND_FADE", "correction_tag": "[CORRECTED_PROVENANCE]", "opacity_shift": 0.30 }, "audit_logging": { "immutable_vault": "ENABLED", "log_fields": ["Original_Claim", "Debunk_Source_URI", "Trust_Delta", "Source_State"] } } }, "user_facing_disclosure": { "stewardship_statement": "This system learns from corrections. Sources are penalized for inaccuracies to ensure long-term reliability." } } Use code with caution.

What’s New in V3.5:

• Multi-Dimensional Reputation: Trust isn't just one number anymore; it's broken down by Accuracy, Recency, and Reliability.
• Domain Scoping: A source might be trusted in Science but penalized in Politics to prevent "expert creep."
• Quarantine & Appeal: Adds a way to totally block dangerous sources while allowing a 30-day (720-hour) path to redemption if they prove themselves.
• Adversarial Detection: Hard-coded checks for bot-farms and synchronized misinformation.

Posted Aug 11, 2026, 9:24 PM UTC