AI Governance & Development Policy
The ethical tenets, governance structures, development lifecycle requirements, and regulatory compliance frameworks that ensure every AI capability is built and deployed responsibly.
9 foundational ethical tenets guiding all AI development
Governance-gated AI development lifecycle
FERPA-aligned with RBAC and encryption
Full TEVV testing before any deployment
Nine foundational tenets
Adapted from The Ethical Framework for AI in Education, these tenets provide the ethical compass for all AI development and deployment decisions at Infinize.
Achieving Educational Goals
AI advances well-defined educational objectives grounded in evidence that benefit learners. Every feature maps to a measurable outcome.
Forms of Assessment
AI broadens the scope of learner talents it assesses, recognizing diverse strengths beyond GPA including skills, engagement, and career readiness.
Administration & Workload
AI enhances institutional efficiency while preserving human relationships. Tools augment, never replace, advisor and faculty judgment.
Equity
AI systems promote equity and avoid discrimination. Bias detection and mitigation are implemented across all predictive models and recommendations.
Learner Autonomy
AI empowers learners to take control of their development. Recommendations present options rather than prescribing outcomes.
Privacy
Balance between privacy and legitimate educational data use. FERPA-aligned architecture with encryption, RBAC, and strict data governance.
Transparency & Accountability
Humans are ultimately responsible for educational outcomes. Explainable outputs ensure stakeholders understand AI-driven recommendations.
Informed Participation
Stakeholders understand AI's implications to make informed decisions. Comprehensive training for all personas on capabilities and limitations.
Ethical Design
AI tools are developed by people who understand their impact on education. Diverse stakeholders are consulted throughout the design process.
AI development lifecycle
Every AI system progresses through governance-gated stages aligned with the NIST AI RMF's GOVERN, MAP, MEASURE, and MANAGE functions.
Ideation & Purpose
Educational goal alignment, context documentation, ethical tenet mapping, and go/no-go decision
Data Governance
Data provenance, FERPA compliance, bias assessment, data minimization, quality standards
TEVV
Functional, bias, safety, privacy, explainability, and human oversight testing with independent review
Monitor & Manage
Continuous monitoring, periodic re-evaluation, model retraining, stakeholder feedback, audit logging
Compliance frameworks
Infinize aligns with multiple regulatory frameworks to ensure comprehensive coverage of privacy, safety, equity, and accessibility requirements.
FERPA
Student data privacy with legitimate educational interest, RBAC enforcement, and data retention controls.
- Legitimate educational interest validation
- Role-based access control enforcement
- Data retention and lifecycle management
- Student rights and consent workflows
NIST AI RMF
Four core functions, GOVERN, MAP, MEASURE, MANAGE, embedded in governance and development lifecycle.
- Risk categorization and assessment
- Continuous monitoring and measurement
- Incident response and management
- Stakeholder engagement processes
State Privacy Laws
SOPIPA and state student data privacy laws tracked and reviewed quarterly.
- SOPIPA compliance tracking
- State-specific privacy requirements
- Quarterly regulatory review cadence
- Proactive monitoring for new legislation
Want to understand our governance in depth?
Our team can walk you through every layer of our AI governance framework, from ethical tenets to compliance documentation.