Develop and implement AI policies that enable innovation while managing risk effectively.

Core Policy Areas

AI Development Policy

Key Policy Elements:
1. Model development standards and documentation
2. Training data requirements and restrictions
3. Testing and validation requirements
4. Model card and documentation mandates
5. Approval gates before production deployment
6. Post-deployment monitoring requirements

Acceptable Use Policy

Define clear boundaries:
PERMITTED:
- Customer service automation with human oversight
- Internal productivity tools
- Predictive analytics for business decisions
- Content generation with human review

RESTRICTED (enhanced review required):
- Hiring and HR decisions
- Credit scoring and financial decisions
- Healthcare diagnoses support

PROHIBITED:
- Autonomous high-stakes decisions without human review
- Systems that discriminate on protected characteristics
- Surveillance or social scoring

Data Governance for AI

• Data quality requirements for training datasets
• Consent requirements for personal data use
• Data retention and deletion policies
• Cross-border data transfer restrictions
• Third-party data source vetting

Model Risk Management

Model Risk Framework

Risk Assessment Criteria:
├── Decision Impact (financial, safety, rights)
├── Degree of Automation (human in loop or not)
├── Data Sensitivity (PII, special categories)
├── Scale (number of affected individuals)
└── Explainability (black box vs interpretable)

Risk Levels:
- LOW: Internal productivity tools, low stakes
- MEDIUM: Customer-facing with human review
- HIGH: Consequential autonomous decisions
- CRITICAL: Safety-critical systems

Validation Requirements by Risk Level

Policy Communication

• Annual training for all AI practitioners
• Role-specific training for high-risk AI users
• Clear escalation paths for policy questions
• Accessible policy documentation portal