AI governance is the set of policies, standards, processes, and accountability structures that an organization or authority establishes to ensure that artificial intelligence systems are developed and used safely, ethically, and in compliance with the law. It ranges from public regulation (such as the EU AI Act) to internal corporate frameworks for overseeing the lifecycle of their models.
It matters because AI poses specific risks—discriminatory biases, opaque decisions, data leaks, or malicious uses—that are not mitigated by technology alone. Good governance is usually structured around several pillars:
- Transparency and explainability of systems and their decisions.
- Accountability, assigning clear responsibilities.
- Risk management and regulatory compliance.
- Fairness and data protection for the individuals affected.
In practice, this translates into ethics committees, model audits, mandatory documentation, and the classification of systems according to their risk level, so that higher-impact applications receive stricter controls before their deployment.