Framework Details
- Frameworks
- Artificial Intelligence Framework
Artificial Intelligence is no longer experimental — it is embedded in national infrastructure, financial systems, public services, and critical decision-making. Yet the global certification landscape remains fragmented, overly theoretical, and disconnected from operational risk.
The GCAF AI Accreditation Framework establishes a rigorous, evidence-based structure for certifying AI-focused programs, certifiers, and training bodies. It is engineered for modularity, auditability, and regional adaptation — while remaining benchmarked against the highest international norms.
Scope of Application
This framework applies to:
- Certification bodies delivering AI ethics, governance, auditing, or development tracks
- Institutions offering AI-related courses, bootcamps, or skilling programs
- Public or private programs designed to train AI developers, users, or evaluators
- Any training or testing system referencing:
- Explainability
- Model governance
- Data ethics
- Bias mitigation
- Risk-based deployment strategies
Alignment & Reference Standards
Certification Categories under This Framework 
Accreditation Criteria (Excerpt)
Each program is reviewed against:
- Curriculum coverage of ISO/IEC 42001 and NIST AI RMF core functions
- Demonstrated application of risk tiering (minimal, high-risk, prohibited)
- Case-based training on real-world AI failures and mitigation design
- Bias detection methods (data provenance, protected class handling)
- Alignment with GCAF’s Responsible Innovation Declaration
- Instructor qualification (practitioner + academic blend)
- Data use protocols (collection, consent, labeling ethics)
- Local legal compliance: GDPR, AI Act, or local equivalents
- Explainability tools and post-deployment monitoring practices
- Inclusion of global majority/non-Western contexts in curriculum