Why It Matters
AI systems are increasingly making decisions that affect people's lives — hiring, lending, healthcare, and law enforcement. Without governance, AI can produce biased outcomes, violate privacy, cause harm, and create legal liability. The EU AI Act now legally requires AI literacy and governance measures. Organizations that implement governance early gain a competitive advantage and reduce regulatory risk.
Key Components of AI Governance
1. AI Policy
A written policy defining:
- Permitted and prohibited AI uses within the organization
- Ethical principles guiding AI deployment
- Roles and responsibilities for AI oversight
- Data handling rules for AI training and inference
- Incident reporting and response procedures
2. Risk Management
- Risk classification — categorize AI systems by risk level (aligned with EU AI Act categories)
- Impact assessments — evaluate potential harms before deployment
- Monitoring — track AI performance, accuracy, and fairness in production
- Human oversight — define when and how humans review AI decisions
3. Data Governance for AI
- Training data quality and bias assessment
- Data protection compliance (GDPR, CCPA)
- Data lineage and documentation
- Consent and legal basis for data used in AI
4. Transparency and Explainability
- Document how AI systems make decisions
- Provide explanations to affected individuals
- Label AI-generated content and interactions
- Maintain technical documentation
5. Accountability
- Designate responsible individuals for AI systems
- Board-level oversight and reporting
- Audit trails and compliance records
- Vendor assessment for third-party AI
Regulatory Drivers
- EU AI Act — mandatory risk management, technical documentation, human oversight
- EU AI Act Article 4 — AI literacy requirement for all organizations
- GDPR Article 22 — rights related to automated decision-making
- ISO 42001 — international standard for AI Management Systems
- NIST AI Risk Management Framework — US voluntary framework
- OECD AI Principles — international governance guidelines
Building an AI Governance Program
- Inventory — identify all AI systems in use (including third-party tools like ChatGPT)
- Classify — assess risk level of each system
- Policy — create or update AI use policy
- Roles — assign AI governance responsibilities
- Train — ensure staff have AI literacy
- Monitor — implement ongoing oversight
- Review — quarterly policy and risk reviews
Key Regulation
- EU AI Act (Regulation 2024/1689) — the primary regulatory driver
- ISO/IEC 42001:2023 — AI Management System standard
- NIST AI RMF — US AI risk management framework
- OECD AI Principles — adopted by 46 countries