AI Governance
The framework of policies, processes, and responsibilities for the responsible development, deployment, and use of AI systems in organizations.
AI Governance = rules, roles, and processes for responsible AI use. Covers ethics boards, risk assessments, compliance (EU AI Act), and clear responsibilities.
Explanation
AI governance encompasses ethical guidelines, risk assessments, compliance requirements (like EU AI Act), transparency obligations, bias monitoring, and clear responsibilities. It ensures AI deployment aligns with company values and legal requirements.
Marketing Relevance
Marketing teams must consider AI governance to avoid reputational risks, ensure GDPR compliance with AI personalization, and address ethical concerns in algorithmic advertising.
Example
A company establishes an AI Ethics Board that reviews all marketing AI tools before deployment, conducts bias audits, and develops guidelines for transparent AI-driven customer communication.
Common Pitfalls
Too strict governance can hinder innovation. Unclear responsibilities lead to governance gaps. Fast AI development outpaces static guidelines. Balance between agility and control.
Origin & History
OECD AI Principles (2019) and EU High-Level Expert Group on AI laid the foundations. EU AI Act (2024) made governance a legal obligation. ISO/IEC 42001:2023 defines AI management system standards.
Comparisons & Differences
AI Governance vs. AI Ethics
AI Ethics defines moral principles; AI Governance implements them into concrete processes, roles, and controls.
AI Governance vs. Model Governance
Model Governance focuses on individual model lifecycles; AI Governance encompasses the entire organization and strategy.