Lattice
Workspace
Responsible AI · Governance workspace

The cartographer's table for AI governance.

Lattice helps teams compare, contrast, and apply six major ethical AI frameworks — without flattening the meaningful differences between human-rights ethics, classification taxonomies, certification methodology, Indigenous data sovereignty, adversarial security, and risk management.

Lattice does not provide legal advice, issue certifications, or produce OCAP® determinations. Every claim on screen carries a source, version, and last-reviewed date.

Honest cartography

Show relationships, not equivalences. Lattice rejects the string ‘equivalent’ outright. Disagreement between frameworks is information, not noise.

Provenance always visible

No claim without a source. Every excerpt links to the canonical document, with version and last-reviewed date on screen.

Stewardship over speed

For OCAP® and adversarial security content, friction is intentional. The tool routes to community engagement and editorial review, not auto-determinations.

Spectrum

Six frameworks, six different jobs.

They're not interchangeable. Lattice positions each along the axes that actually matter: from values to controls, from voluntary to enforceable, from global to community-specific.

Open the comparison
VoluntaryEnforceableControlsMixedAdversarialValues →
MAP-1.1 Context is establishedGOVERN-2.1 Roles and responsibilitiesAML.T0043 Craft adversarial dataRecommendation 16 Human oversightOECD 1.2 Human-centred valuesPRV-04 Privacy by designACC-02 Auditability of decisionsOwnership · Control · Access · PossessionMEASURE-2.7 AI performance under uncertaintyAML.T0040 Manipulate ML modelArticle 4.2 Trustworthy AITRA-03 Transparency to end usersMAP-1.1 Context is establishedGOVERN-2.1 Roles and responsibilitiesAML.T0043 Craft adversarial dataRecommendation 16 Human oversightOECD 1.2 Human-centred valuesPRV-04 Privacy by designACC-02 Auditability of decisionsOwnership · Control · Access · PossessionMEASURE-2.7 AI performance under uncertaintyAML.T0040 Manipulate ML modelArticle 4.2 Trustworthy AITRA-03 Transparency to end users

Bring a system to the table.

Run the intake, see how every framework reads it, and walk out with a defensible draft. Pre-loaded with four sample systems so you can try it without signing up.

Command palette

Search frameworks, systems, glossary, and pages