Glossary

An AI governance framework is the set of roles, policies, and controls used to manage AI systems across their lifecycle.

AI risk management is the process of identifying, assessing, mitigating, and monitoring risks from AI systems across their lifecycle.

Data ethics is the responsible use of data: collecting, sharing, and processing it in ways that respect people, context, and legitimate expectations.

Model accountability is having clear ownership, traceability, and responsibility for how an AI model is built, changed, and used.

Responsible AI is the practice of designing, deploying, and operating AI systems in a lawful, ethical, and safe way, with clear accountability.