RAIT Framework
A responsible AI transfer framework for making governance capacity a condition of access to progressively riskier AI systems.
Access to powerful AI should be earned through stewardship
RAIT responds to a practical governance gap: AI technologies cross borders faster than many institutions can build the legal, technical, and civic capacity needed to govern them.
Why RAIT matters
Technology transfer is not neutral when the receiving context lacks oversight capacity. RAIT turns the moment of transfer into a governance checkpoint: where readiness exists, technology flows; where it does not, capacity must be built first.
A governance ladder for responsible AI transfer
RAIT links access to progressively riskier AI systems with demonstrated governance maturity. The aim is not to block technology, but to make adoption safer, more accountable, and less dependent.
Data protection law, independent enforcement, human-rights commitments, and a multi-stakeholder AI task force.
Rules for high-risk sectors, regulatory sandboxes, public AI registries, and procurement training.
AI safety or audit capacity, incident monitoring, contestation rights, and transparent public procurement standards.
Participation in standards bodies, domestic innovation capacity, and responsible governance models that can travel.
The contract is where principles become enforceable
RAIT proposes standard modules for AI technology transfer agreements so capacity-building, rights protection, auditability, training, and exit pathways are built into the deal itself.
Governance capacity-building plan
A required part of the transfer agreement that makes responsible AI adoption measurable, reviewable, and harder to treat as a voluntary promise.
Human rights impact assessment
A required part of the transfer agreement that makes responsible AI adoption measurable, reviewable, and harder to treat as a voluntary promise.
Data governance and sovereignty clause
A required part of the transfer agreement that makes responsible AI adoption measurable, reviewable, and harder to treat as a voluntary promise.
Transparency and auditability requirements
A required part of the transfer agreement that makes responsible AI adoption measurable, reviewable, and harder to treat as a voluntary promise.
Technical training and upskilling mandate
A required part of the transfer agreement that makes responsible AI adoption measurable, reviewable, and harder to treat as a voluntary promise.
Exit and transition clause
A required part of the transfer agreement that makes responsible AI adoption measurable, reviewable, and harder to treat as a voluntary promise.
Governance capacity has to be tested, not assumed
The framework calls for independent verification through peer review, civil society monitoring, audits, and a multi-stakeholder council that can assess readiness and resolve disputes.
| Indicator | Value | Status |
|---|---|---|
| Foundational readiness | Stage 1 | Low-risk systems |
| Sector regulation | Stage 2 | Medium-risk systems |
| Advanced oversight | Stage 3 | High-risk systems |
| Governance leadership | Stage 4 | Co-development |
Compliance logic
RAIT works only if access, obligations, verification, and consequences are connected. Without that connection, governance remains aspirational.
A framework for governance, advisory work, and training
RAIT gives CentPol a serious public framework for responsible AI adoption in emerging technology markets. It can support research, partner advisory work, governance training, and policy sprint design.
Framework paper
Use RAIT as a public research asset for AI governance capacity, technology transfer, and institutional readiness.
Governance curriculum
Translate RAIT into exercises on risk, oversight, auditability, procurement, and accountability.
Partner diagnostic
Use RAIT to assess readiness before high-risk AI procurement, deployment, or transfer agreements.