Published and working methodologies
Published methodology and historical scores remain unchanged. The seven-pillar Responsible AI architecture is a research draft awaiting reconciliation and accountable-owner approval.
Alpha • Responsible AI
Connect principles to evidence.
Responsible AI belongs within one versioned governance architecture.
Talk to AlphaOne control, multiple references
Map a control to relevant standards without counting the same evidence twice. A mapping is an interpretation, not certification or a determination of legal compliance.
An auditable chain
Document the system and scope, applicable requirement, control owner, evidence source, assessment method, reviewer and decision. Preserve revisions and unresolved disputes.
Machine-readable resources
Markdown representation
The same educational content in machine-readable form. /responsible-ai/framework.md
Alpha Evals
Explore existing readiness and independent evaluation pathways. https://evals.alpha.ac/
Developer documentation
Discover existing governed API and MCP services. https://docs.alpha.ac/
Explore
Choose the relevant path.
Responsible AI requires accountable governance.
Principles are important. Evidence is essential. Alpha connects the standards, controls, decisions and independent evaluations that make enterprise AI governable.
ExploreChoose the assessment that fits the evidence.
Public disclosures and confidential enterprise evidence support different kinds of evaluation.
ExploreAsk for decisions, owners and evidence.
Board oversight connects AI strategy to authority, risk appetite and accountable execution.
ExploreTransparency starts with bounded claims.
This disclosure framework is an editorial draft. It does not assert certifications, completed audits or independently verified assurance.
ExploreEvidence supports a conclusion only within its stated scope.