Consequence and control engine
data/build-012-engine-v1.jsonNine evidence-bearing dimensions, proportional required controls, inherited human-review gate and explicit non-approval boundary.
BUILD 012
AI Use Consequence & Control Mapper
AI controls should follow the consequence of a bounded use—not the prestige of a model or a generic risk label.
QUESTIONWhat controls does this AI use earn?
STRUCTURED PUBLIC BUILD ARCHIVE
Research, creation, decisions, audits and plans are publicly structured without claiming that every release gate is complete.
This page exists so the durable record does not depend on conversation history. Research, decisions, generated artifacts, audits, limitations, media metadata, plans and canonical source paths are retained here as part of the build itself. A listed repository path proves that an implementation record was retained; it does not independently support a real-world factual claim.
5 of 5 listed retained paths have a public reader link on this archive page. Unlinked paths remain named for traceability but are not represented as publicly inspectable evidence.
What informed the build
NIST AI RMF 1.0
Grounds context-sensitive, rights-preserving management of AI risk across the lifecycle.
OECD AI classification framework
Grounds classification by people, context, data, model, task and output rather than model name alone.
What exists because we built it
012-A AI Use Consequence & Control Mapper
Score an evidenced use, edit eleven controls, expose gaps and export locally.
012-B Controls Follow Consequence
Three interactive consequence states visibly reconfigure control load.
AI Control Mapper Engine v1
Reusable consequence, coverage, inherited human-review, structural-gate and escalation assessment.
Why it works this way
Builds 001 and 010 are concrete dependencies
The engine directly calls the human-review score/grade primitives and assessIdeaSkeleton.
Consequence drives required controls
Severe rights or essential-service impacts trigger the full control set regardless of average score.
Missing controls escalate
A severe use with an incomplete control architecture returns ESCALATE / DO NOT DEPLOY.
No state grants permission
The boundary excludes legal classification, compliance, safety certification and deployment approval.
How the work was checked
Machine-readable archive
data/build-012-archive-v1.jsonRoutes, lineage, privacy and consumers.
Privacy/support
data/build-012-privacy-support-v1.jsonClient-local assessment and competent-review boundary.
Freshness/performance
data/build-012-freshness-performance-v1.jsonCurrent primary sources and offline core value.
Browser fixtures
tests/build012.spec.tsControlled, escalation, reset, export, lifecycle, 320px and B-state coverage.
What this build intentionally leaves forward
Release path
Complete static gates, Chromium/WebKit matrix, media, preview and exact-head certification.
Future consumers
013, 014, 019, 021, 022, 025, 032, 033, 038, 040, 041 and 042 consume cap:012.
Take the social artifact with you
B-LINKEDIN / CANONICAL FILM
720×900 · 4:5 MP4 · 24 fps · silent-first
CURRENT VERIFIED MEDIA / 30-second film · 382.4 KB
FINAL PROPOSITION / CONTROLS SHOULD FOLLOW CONSEQUENCE.
SHA-256 3ef7a721c152af73e1d66cdc2bbec63ed7e2d5c46c432463ce70dba6e2ec7234Reproducible from data/linkedin-film-specs-v1.json with scripts/render-linkedin-films.py. The website—not a chat attachment—is the canonical media source.
The repository files that preserve the implementation record
apps/web/app/100-builds/012/a/AIControlMapper.tsxapps/web/app/100-builds/012/b/ConsequenceField.tsxpackages/release/src/decision-engines.tsdata/build-012-engine-v1.jsondata/build-012-archive-v1.jsondocs/builds/012/SPEC.mddocs/builds/012/ARCHIVE.mdtests/build012.spec.tsThese paths preserve the implementation record. Only items with an explicit public reader link are publicly inspectable here. A file path, working interface or internal engine is not independent support for a real-world claim; synthetic propositions and design reasoning remain labeled within the retained record.