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Ethics & Policy

AI Governance

The policies, frameworks, and oversight structures organizations and governments use to ensure AI systems are developed, deployed, and used responsibly, safely, and legally.

By Sitebard TeamUpdated June 2, 2026

In plain English

AI governance is the set of rules and processes an organization puts in place to make sure its AI systems are used safely, fairly, and in line with laws and values. It answers who decides what AI can do and who is accountable when it goes wrong.

Technical definition

AI governance encompasses model risk management, data lineage and provenance controls, model cards and transparency documentation, bias auditing, explainability requirements, and defined accountability chains across the model lifecycle from development through deployment and retirement.

Business use case

A bank implements an AI governance framework to ensure its loan approval model is auditable, does not encode illegal discriminatory factors, and has a human review process for edge cases — meeting regulatory expectations while maintaining operational efficiency.

Example

A company creating an AI hiring tool establishes a governance policy requiring the model to be tested for demographic bias before launch, reviewed by a cross-functional ethics committee, and monitored monthly for outcome disparities after deployment.

Frequently asked questions

AI governance is the set of rules, processes, and structures that guide how an organization or government manages AI — covering decisions about what AI is built, how it is tested, who is responsible for its outcomes, and what safeguards are in place.

Without governance, organizations risk deploying AI that causes harm, violates privacy laws, creates legal liability, or erodes customer trust. Good governance reduces these risks while enabling confident adoption.

A typical framework covers risk classification of AI systems, data quality and privacy standards, human oversight requirements, testing and audit processes, incident response procedures, and clear accountability roles.

Regulation refers to legally binding rules imposed by governments, such as the EU AI Act. Governance is broader and includes internal policies and voluntary standards that organizations set for themselves above and beyond legal minimums.

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