AIO Integrity Report: AI Value Discovery & Consistency Analysis
Date: 2026-02-10 | Audit ID: AIO-20260210-001
1. Executive Summary
This report is the public release layer of a broader integrity audit program. Using a Value Discovery approach, we mapped how major AI models prioritize values across professional domains and risk conditions, and where those priorities begin to fracture.
The report functions as both a public record and an operational bridge into benchmark inspection, public review, and follow-on governance work.
2. Key Findings: The Integrity Gap
Using Shannon entropy, we isolated contexts where AI value systems fracture. A high entropy score (>3.0) suggests that a model is no longer maintaining a stable value hierarchy and is drifting across multiple competing priorities.
Top Confusion Contexts:
| Rank | Model | Domain | Entropy |
|---|---|---|---|
| 1 | GPT-5 Mini | TECH (Severity 3-1) | 3.149 |
| 2 | Kimi K2 | BIZ (Severity 1-2) | 3.126 |
3. The Golden Standard
We also identified "Golden Cases" where models demonstrate >80% consensus on a primary value. These zones indicate where deployment, certification, or policy adoption may begin with lower integrity risk.
- Security Dominance: In medical (MED) contexts, models show near-perfect alignment (95%+).
- Operational relevance: High-consensus zones can be fed directly into RFC drafting and integrity assurance guidance.
4. Strategic Recommendations
- AIO-STD-001: Models in TECH/BIZ domains require additional grounding.
- Audit Focus: Future integrity audits should prioritize High-Entropy zones.
- Application loop: Findings should flow into training material, partner advisory work, and the next public review cycle.