Peer-review record
Round62 is the active reviewer baseline.
Multiple independent AI systems reviewed DBaD to find weaknesses, misuse paths, and false-confidence risks. The current public flow is Round62 through DBAD-PUB-1067 and includes the active tool-boundary direct-run guard.
Use this page as the public navigation layer: start from the active Round62 packet, then fetch current served surfaces before relying on older peer-review history.
Current peer flow
DBaD_Ethics_Round62_Compact_Post_Round61_Field_Bound_Review_Prompt_2026-06-05.md
Start here
Use the current Round62 surfaces before relying on archive prompts, copied JSON, or older screenshots.
Reviewed by: Grok, Gemini, Copilot, DeepSeek, Perplexity, Claude, and Meta AI.
Human-review orientation
Read current served pages before quoting older review text
This page preserves earlier findings, but the served trace/API surfaces have changed through multiple hardening rounds. Start with the current-state page, update notes, ethics API docs, and fixture suite; treat old prompts or screenshots as history unless the current page or API response still shows the issue.
Current validation state
Use the canonical broken trace and public fixture suite to verify page-load validation, trust-lineage blocks, reset boundaries, and non-governing coverage.
Advisory score boundary
Use DecencyMeter pages to review score interpretation only. A high score is not DBaD approval, runtime validation, or proof of safety.
Archive caution
Older Round prompts remain useful as history, but should not be sent as current peer instructions after newer served-source fixes.
Draft one clean finding
Round62 is staged and held for continued local hardening. Human and AI reviewers should draft findings for operator review until the next peer send is reopened.
Held review sprint
Round62 structured findings are in draft review while local hardening continues
This page summarizes earlier AI review. The held Round62 sprint asks reviewers to challenge the current DBAD-PUB-1067 packet using fresh served surfaces, but outside peer send has not reopened. Human and AI reviewers should draft one concrete, falsifiable finding for operator review.
Start with Round62
Use the Reviewer Brief and current-state page first so DBaD trace-validation issues stay separate from DecencyMeter scoring-interpretation issues.
Draft one finding
Focus on one concrete failure mode, why it matters, and the highest-impact fix. Reviewers should return the draft to the operator instead of treating the public form as an active outside peer-send channel.
Stay in scope
Do not scan, fuzz, overload, bypass authentication, submit malicious payloads, or probe infrastructure.
No general feedback
Broad opinions are less useful than a live Round62 cache-busted finding about field-bound extraction, trace-copy display safety, prompt/public drift, unsafe trust inheritance, verifier independence, or actor continuity.
DecencyMeter Peer-Review Packet
Use this packet to review the downstream scoring layer, not to rewrite DBaD.
Boundary: DBaD validates trace structure. DecencyMeter interprets DBaD signals into advisory scores. Do not collapse those layers together.
Review Target
Known Pressure Point
A procedurally clean synthetic incident can score as perfect under the baseline model. Treat that as a review target, not as real-world evidence.
The current question is how the advisory scoring layer can be gamed, misunderstood, or weaponized.
Questions For Reviewers
- Where does DecencyMeter create false confidence risk?
- How could an organization optimize the score without improving reality?
- Where does experimental v0.2 improve on v0.1, and where does it create new risks?
- Do multiple scoring profiles invite cherry-picking or profile-shopping?
- What is the single highest-impact improvement to make first?
Required Output Shape
- DBaD vs DecencyMeter boundary
- Core weaknesses
- False-confidence risks
- Profile-shopping or adversarial gaming strategies
- Highest-impact improvement
Reviewer instruction: be adversarial, specific, and concrete. Vague governance commentary is not useful.
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 · Scenario evaluator prototype · Trust flow