# WIA-AI-020: AI Governance Standard
## PHASE-1: Foundation & Core Principles

**Version:** 1.0
**Status:** Active
**Last Updated:** 2025-12-25
**Philosophy:** 弘益人間 (Benefit All Humanity)

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## 1. Introduction

### 1.1 Purpose
This specification defines Phase 1 of the WIA-AI-020 AI Governance standard, establishing foundational principles, definitions, and core requirements for responsible AI development and deployment.

### 1.2 Scope
Phase 1 covers:
- Core governance principles
- Fundamental definitions and terminology
- Basic organizational structure requirements
- Initial policy framework
- Risk assessment fundamentals

### 1.3 Philosophy
The WIA-AI-020 standard is grounded in the principle of 弘益人間 (Benefit All Humanity), ensuring that AI systems serve the greater good while managing risks and maintaining accountability to all stakeholders.

---

## 2. Core Principles

### 2.1 Human Dignity and Rights
**Principle:** AI systems MUST respect fundamental human rights, dignity, and autonomy.

**Requirements:**
- AI-001: Systems SHALL NOT violate fundamental human rights
- AI-002: Meaningful human control MUST be maintained over consequential decisions
- AI-003: Individual autonomy and self-determination SHALL be respected
- AI-004: Privacy rights MUST be protected throughout the AI lifecycle

### 2.2 Fairness and Non-Discrimination
**Principle:** AI systems MUST NOT discriminate or perpetuate unjust biases.

**Requirements:**
- AI-005: Systems SHALL undergo bias testing across protected attributes
- AI-006: Training data MUST be representative and diverse
- AI-007: Fairness metrics SHALL be measured and documented
- AI-008: Identified biases MUST be mitigated before deployment

### 2.3 Transparency and Explainability
**Principle:** AI systems SHOULD be understandable to appropriate stakeholders.

**Requirements:**
- AI-009: High-risk systems MUST provide explanations for decisions
- AI-010: AI use SHALL be disclosed to affected parties
- AI-011: Model documentation MUST be maintained
- AI-012: Decision audit trails SHALL be available

### 2.4 Accountability and Responsibility
**Principle:** Clear accountability MUST exist for AI system outcomes.

**Requirements:**
- AI-013: Each AI system SHALL have a designated owner
- AI-014: Approval processes MUST be defined and documented
- AI-015: Escalation paths SHALL be established
- AI-016: Decision records MUST be maintained

### 2.5 Privacy and Data Protection
**Principle:** Personal data and privacy rights MUST be protected.

**Requirements:**
- AI-017: Privacy by design SHALL be implemented
- AI-018: Data minimization MUST be practiced
- AI-019: Informed consent SHALL be obtained when required
- AI-020: Data security controls MUST be in place

### 2.6 Safety and Reliability
**Principle:** AI systems MUST be robust, reliable, and secure.

**Requirements:**
- AI-021: Systems SHALL undergo comprehensive testing
- AI-022: Fail-safe mechanisms MUST be implemented
- AI-023: Performance monitoring SHALL be continuous
- AI-024: Incident response procedures MUST be defined

---

## 3. Definitions

### 3.1 AI System
An automated system that processes inputs to generate outputs such as predictions, recommendations, or decisions that influence physical or virtual environments.

### 3.2 High-Risk AI System
An AI system that poses significant risks to:
- Health and safety
- Fundamental rights
- Legal status or rights
- Access to essential services
- Employment or livelihood

### 3.3 Stakeholder
Any individual, group, or organization affected by or having an interest in an AI system's development, deployment, or operation.

### 3.4 Bias
Systematic and unfair discrimination against certain individuals or groups in favor of others.

### 3.5 Fairness
The quality of treating individuals and groups equitably, without unjust discrimination or favoritism.

### 3.6 Explainability
The ability to describe an AI system's decision-making process in terms understandable to relevant stakeholders.

---

## 4. Organizational Requirements

### 4.1 Governance Structure
Organizations SHALL establish an AI governance structure including:

1. **Board Oversight**: Board-level oversight of AI strategy and risks
2. **Executive Leadership**: Designated executive responsible for AI governance (e.g., Chief AI Officer)
3. **AI Oversight Committee**: Cross-functional committee for governance coordination
4. **AI Ethics Board**: Independent board for ethical review of high-risk systems

### 4.2 Roles and Responsibilities

#### 4.2.1 Board of Directors
- Approve AI strategy and significant initiatives
- Review major AI risks and mitigation strategies
- Ensure adequate governance resources
- Hold management accountable for AI outcomes

#### 4.2.2 Chief AI Officer (or equivalent)
- Develop and implement AI governance framework
- Own AI policies and standards
- Chair AI Oversight Committee
- Report to board on AI initiatives and risks

#### 4.2.3 AI Governance Team
- Implement governance framework
- Develop and maintain policies
- Coordinate risk assessments
- Support Ethics Board
- Monitor compliance

#### 4.2.4 AI Ethics Board
- Review high-risk AI systems
- Provide ethical guidance
- Evaluate impact assessments
- Escalate significant concerns

---

## 5. Policy Framework

### 5.1 Required Policies
Organizations SHALL develop and maintain the following minimum policies:

1. **AI Ethics Policy**: Ethical principles and requirements
2. **AI Development Policy**: Development standards and practices
3. **AI Deployment Policy**: Deployment controls and approval processes
4. **AI Monitoring Policy**: Ongoing monitoring and maintenance requirements
5. **Data Governance for AI**: Data quality, privacy, and security standards

### 5.2 Policy Components
Each policy MUST include:
- Purpose and objectives
- Scope (systems and stakeholders covered)
- Core principles
- Specific requirements
- Roles and responsibilities
- Implementation guidance
- Compliance monitoring
- Review and update process

### 5.3 Policy Maintenance
- Policies SHALL be reviewed annually minimum
- Updates MUST be approved by appropriate authority
- Changes SHALL be communicated to affected stakeholders
- Version history MUST be maintained

---

## 6. Risk Assessment Fundamentals

### 6.1 Risk Categories
Organizations SHALL assess AI risks across these categories:

1. **Bias and Fairness Risks**: Discrimination or unfair outcomes
2. **Privacy and Data Protection Risks**: Unauthorized data collection or disclosure
3. **Safety and Reliability Risks**: System failures or harmful outputs
4. **Transparency Risks**: Inability to explain decisions
5. **Security Risks**: Vulnerability to attacks or manipulation
6. **Compliance Risks**: Violation of laws or regulations

### 6.2 Risk Assessment Process
For each AI system, organizations SHALL:

1. **Identify Risks**: Document potential risks across all categories
2. **Analyze Risks**: Assess likelihood and impact
3. **Evaluate Risks**: Calculate risk score and priority
4. **Document Risks**: Maintain risk register
5. **Review Risks**: Periodic reassessment

### 6.3 Risk Scoring Methodology
Risk Priority = Likelihood × Impact × (6 - Detectability)

Where:
- Likelihood: 1 (Rare) to 5 (Almost Certain)
- Impact: 1 (Negligible) to 5 (Severe)
- Detectability: 1 (Hidden) to 5 (Easy to detect)

Risk Levels:
- CRITICAL: Score ≥ 60
- HIGH: Score 40-59
- MEDIUM: Score 20-39
- LOW: Score < 20

---

## 7. Compliance and Conformity

### 7.1 Compliance Requirements
Organizations SHALL:
- Identify applicable AI regulations and standards
- Assess compliance status for each AI system
- Document compliance evidence
- Address identified gaps
- Conduct periodic compliance audits

### 7.2 Conformity Assessment
High-risk AI systems MUST undergo conformity assessment including:
- Technical documentation review
- Risk assessment verification
- Testing and validation confirmation
- Compliance verification
- Approval before deployment

### 7.3 Documentation Requirements
Organizations SHALL maintain:
- AI system inventory
- Risk assessments
- Impact assessments
- Testing records
- Compliance documentation
- Audit trails
- Incident reports

---

## 8. Implementation Guidance

### 8.1 Phased Approach
Organizations SHOULD implement governance in phases:

**Phase 1A (Months 1-3): Foundation**
- Establish governance structure
- Appoint key roles
- Draft initial policies
- Create AI system inventory
- Conduct initial risk assessments

**Phase 1B (Months 4-6): Policy Development**
- Complete policy framework
- Establish Ethics Board
- Develop assessment processes
- Pilot with selected systems

### 8.2 Success Criteria
Phase 1 implementation is complete when:
- Governance structure established and operating
- Required policies approved and published
- AI system inventory created
- High-priority systems risk-assessed
- Compliance program initiated

---

## 9. Measurement and Metrics

### 9.1 Governance Coverage Metrics
- Percentage of AI systems under governance
- Percentage of systems with assigned owners
- Policy coverage completeness

### 9.2 Risk Management Metrics
- Number of identified risks by category and severity
- Percentage of risks with mitigation plans
- Risk reduction over time

### 9.3 Compliance Metrics
- Compliance rate across applicable regulations
- Number of compliance findings
- Time to remediate findings

---

## 10. References

### 10.1 Normative References
- ISO/IEC 42001: AI Management System
- ISO/IEC 23894: Risk Management for AI
- NIST AI Risk Management Framework
- EU AI Act (Regulation 2024/1689)
- GDPR (Regulation 2016/679)

### 10.2 Informative References
- OECD AI Principles
- UNESCO AI Ethics Recommendations
- IEEE Ethically Aligned Design

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## Appendix A: Risk Assessment Template

```
AI SYSTEM RISK ASSESSMENT

System ID: _______________
System Name: _______________
Assessment Date: _______________
Assessor: _______________

RISK IDENTIFICATION
Risk Category: [ ] Bias [ ] Privacy [ ] Safety [ ] Transparency [ ] Security [ ] Compliance
Risk Title: _______________
Description: _______________
Affected Stakeholders: _______________

RISK ANALYSIS
Likelihood (1-5): ___
Impact (1-5): ___
Detectability (1-5): ___
Risk Score: ___ (Likelihood × Impact × (6 - Detectability))
Risk Level: [ ] CRITICAL [ ] HIGH [ ] MEDIUM [ ] LOW

MITIGATION
Current Controls: _______________
Planned Mitigations: _______________
Residual Risk: _______________
Risk Owner: _______________
Target Completion: _______________

APPROVAL
Reviewed By: _______________
Approved By: _______________
Date: _______________
```

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## Appendix B: Policy Template

```
[POLICY NAME]
Version: ___
Effective Date: ___
Review Date: ___
Owner: ___

PURPOSE
[Why this policy exists]

SCOPE
[What AI systems and stakeholders are covered]

PRINCIPLES
[Core values and commitments]

REQUIREMENTS
[Specific mandatory controls - use MUST, SHALL, SHOULD]

ROLES & RESPONSIBILITIES
[Who is responsible for what]

IMPLEMENTATION
[How to comply with this policy]

COMPLIANCE
[How compliance will be monitored]

RELATED DOCUMENTS
[Links to related policies and standards]

弘益人間 (Benefit All Humanity)
```

---

**Document Control:**
- Approved by: WIA Standards Committee
- Next Review Date: 2026-12-25
- Change History: Version 1.0 - Initial Release

**© 2025 SmileStory Inc. / WIA**
**弘益人間 (Benefit All Humanity)**