National AI Governance Framework
Trust Is the Foundation
Responsible, transparent, human-centered AI — with the policies, safeguards, and accountability that make adoption safe and sustainable.
Core Principles
How AI Will Be Used in Bahamian Education
Ethics & Fairness
AI must be fair, inclusive, and aligned with Bahamian values.
Transparency
Decisions and data use are explainable and documented.
Human Oversight
Educators make high-stakes decisions; AI advises, never decides alone.
Bias Mitigation
Systems are tested for bias across student groups.
Academic Integrity
Clear norms for appropriate AI use by students & staff.
Student Safety
Age-appropriate, safe, and protective by default.
Policy Explorer
The Governance Framework
🏛️ Governance Structure
A national AI-in-Education Governance Committee sets policy, approves tools, reviews incidents, and oversees ethics — with educator, legal, data-protection, and community representation. Reviews are continuous, not one-time.
🔐 Data Governance & Privacy
Student data is minimized, encrypted, access-controlled, and never sold or used for advertising. Aligned with FERPA/COPPA-style protections and GDPR-ready; clear retention, consent, and deletion rights. Audit trails on all access.
🛒 Vendor Selection & Responsible Procurement
Approved-vendor standards require data-processing agreements (no training on student data), transparency, accessibility, security certifications, and exit/portability terms to avoid vendor lock-in.
🧾 Compliance & Accountability
Clear roles and responsibilities; incident reporting; regular audits; published standards so schools, families, and partners can hold the system accountable.
🔒 Cybersecurity
Defense-in-depth: secure identity (SSO/MFA), least-privilege access, encryption, monitoring, and incident response — protecting the national data platform and school systems.
Risk Management
Risks & Mitigations
| Risk | Mitigation |
|---|---|
| Bias & unfair outcomes | Bias testing; diverse data; human review; equity audits |
| Privacy breach | Encryption, minimization, access control, audits |
| Cybersecurity threats | MFA, monitoring, incident response, vendor security standards |
| Over-reliance on AI | Human-in-the-loop policy; AI-literacy for staff & students |
| Academic integrity | Clear norms; authentic assessment; detection + teaching |
| Vendor lock-in | Open standards; data portability; multi-vendor strategy |
| Teacher acceptance | Co-design; PD; quick wins; champion networks |
| Community trust | Transparency, communication, and consultation |
| Change fatigue | Phased rollout; realistic pacing; support |