What is AI Governance

Definition

AI governance is the framework of policies, roles, and processes that ensures AI systems are developed and used responsibly, addressing accountability, fairness, transparency, risk, and compliance across the lifecycle from data and model development to deployment and monitoring.

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Establishes accountability for how AI systems are built and used
Manages risk around fairness, safety, privacy, and compliance
Provides oversight and documentation needed for emerging AI regulation
Builds organisational and public trust in AI decisions

Real World Example

A bank stands up an AI governance program defining model approval gates, bias reviews, documentation standards, and ongoing monitoring, so every deployed model has accountable owners and a defensible audit trail for regulators.

FAQs

What does AI governance cover?

It covers accountability, fairness, transparency, risk management, documentation, and compliance across the AI lifecycle.

Why is AI governance increasingly important?

Growing regulation and the high stakes of AI decisions make responsible, accountable, auditable AI essential for organisations.

Who is responsible for AI governance?

Typically a cross-functional effort spanning data science, legal, compliance, risk, and leadership, with clearly defined roles.