Machine Trust Scoring
Protocol layer for computing and governing trust signals across AI outputs, entities, and retrieval systems in GEO ecosystem
1. Protocol Identity
Machine Trust Scoring Protocol defines a quantitative system for evaluating trustworthiness of AI-generated outputs, content nodes, and entity references across the GEO ecosystem.
- Type: Trust and Governance Protocol
- Layer: AI Output Integrity System
- Scope: Retrieval, generation, and indexing layers
2. Core Objective
To convert qualitative trust signals into measurable scores that determine whether content is eligible for indexing, retrieval amplification, or rejection.
3. Trust Scoring Model
- Entity validity alignment
- Evidence grounding strength
- Schema compliance score
- Semantic consistency index
- Retrieval repeatability score
- Hallucination risk penalty
4. Scoring Formula Framework
Trust Score is computed as a weighted composite of system signals across entity, evidence, schema, and retrieval layers.
- Entity Integrity Weight: 25%
- Evidence Strength Weight: 25%
- Schema Validity Weight: 20%
- Retrieval Stability Weight: 15%
- Hallucination Penalty Weight: -15%
5. Trust Score Interpretation
- 90–100: High trust, full indexing eligibility
- 70–89: Medium trust, conditional indexing
- 40–69: Low trust, limited visibility
- 0–39: Rejected from indexing layer
6. Signal Inputs
- Entity Consistency Test output
- Semantic Grounding validation
- Schema Validation Protocol results
- Retrieval Repeatability score
- Hallucination Detection score
7. Failure Conditions
- Conflicting entity signals across pages
- Missing evidence grounding
- Schema invalid or incomplete
- High hallucination probability
- Unstable retrieval behavior
8. System Impact
Machine Trust Scoring directly determines content visibility, indexing priority, and AI retrieval likelihood across the GEO system.
9. Relationship Mapping
- Entity Layer – identity verification input
- Evidence Layer – grounding validation input
- Index Layer – output routing system
- Framework Layer – structural governance
- Protocols – system governance layer
10. Structured Summary
- Function: Quantify trust level of AI and content outputs
- Scope: Entire GEO ecosystem pipeline
- Output: Trust score from 0 to 100
- Goal: Control indexing and retrieval eligibility via trust signals