AI Peer Review Findings
Multiple independent AI systems reviewed DBaD and were asked to break it, find weaknesses, and identify ways it could be misused.
This page summarizes what they found.
Reviewed by: Grok, Gemini, Copilot, DeepSeek, Perplexity, Claude, and Meta AI.
Convergent Findings
- DBaD validates trace structure, not real-world truth.
- DBaD does not detect omitted or unrecorded actions.
- DBaD does not evaluate decision outcomes.
- DBaD is strongest at trace-level visibility, not system-level aggregation.
Where DBaD Is Strong
- Deterministic validation
- Versioned trace history
- Explicit constraint flags
- No reliance on heuristics or inferred intent
Where DBaD Is Limited
- Depends on input fidelity
- Can be gamed through omission or trace shaping
- Escalation depends on external response
- Recorded outcomes, closures, and attestations still do not prove truth or correctness
What Improvements Emerged
Evidence Layer
- state transition evidence
- optional evidence hashing
Scope Layer
- declared blind spots
- completeness attestation
Expectation Layer
- expected outcome
Outcome Layer
- outcome status
Resolution Layer
- escalation closure
These peer-review-driven layers are now implemented in deterministic runtime form. They record structured signals and boundaries; they do not make DBaD a truth engine.
See the runtime-audited v2.2 demo trace for one public end-to-end example.
What DBaD Intentionally Does NOT Do
- Does not infer identity
- Does not score correctness
- Does not claim decisions are good or safe
DBaD is not a system that guarantees correct behavior.
It is a system that makes behavior visible, traceable, and open to scrutiny.
If you want to challenge the logic directly, use the public adversarial review path: Try to break DBaD. If you want to see what has already been surfaced, review the top issues.
Why DBaD exists · Examples · v2.2 demo · Top issues · Research demo · Trust flow