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AI governance

AI Governance

AI governance is the set of structures, processes, policies and roles through which an organization manages the development, adoption and control of its AI systems, ensuring they are aligned with legal, ethical and strategic goals. It is not a single document or a single business function, but a governing system that spans technology, legal, risk and business.

Definition

How it works

In practice, an AI governance program defines who within the organization has the authority to approve the use of an AI system, what criteria must be verified before release (data quality, fairness, safety, regulatory compliance), how systems are monitored after deployment, and who intervenes in case of malfunction or a reported issue. It often takes the form of dedicated committees, internal policies and inventory tools tracking AI systems in use.

Applications

For companies, having structured AI governance is not just a requirement for complying with regulations like the EU AI Act, but a competitive factor: it reduces reputational risk, facilitates responsible adoption of new tools, and reassures customers, partners and investors about the organization's ability to manage a complex technology. Standards such as ISO/IEC 42001 provide a structured model for building this kind of governance.

History & etymology

The concept developed rapidly from the second half of the 2010s onward, borrowing practices from data governance and traditional corporate governance, but adding elements specific to machine learning: the probabilistic nature of models, their evolution over time, and the difficulty of anticipating every possible behavior in production.

How it's used in Grace

Grace offers evaluation scenarios dedicated to AI governance, in which professionals must decide how to structure oversight roles and processes for the responsible adoption of AI in their organization.

Related terms

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