AI Governance: Definition and Examples

AI Governance encompasses all institutional, legal, and technical mechanisms aimed at regulating the design, deployment, and use of AI systems. It covers both internal company policies and national and international regulations, such as the European AI Act. The main objective is to ensure that AI is developed and used to maximize its benefits while minimizing risks to individuals and society.

AI governance rests on several fundamental pillars: algorithmic transparency, accountability of actors (developers, deployers, users), fairness and non-discrimination, privacy protection, and system security. It involves establishing audit processes, risk assessments, and recourse mechanisms for people affected by automated decisions.

In practice, AI Governance translates into the creation of ethics committees, drafting AI use charters, compliance with applicable regulations, and adoption of technical standards such as model cards or algorithmic impact assessments. Organizations must also train their teams on ethical issues and rigorously document their development processes.

For prompt engineering practitioners, AI governance is a crucial subject because it directly influences how language models can be used. Understanding governance principles allows designing prompts that respect ethical frameworks, anticipate limits imposed by usage policies, and contribute to responsible use of generative AI tools.