Terminology, Concepts, and Lifecycle of Artificial Intelligence
Description: After completing the course, participants will be able to demonstrate the following competences:
- Use standardized AI terminology and definitions correctly.
- Describe and differentiate phases of the AI lifecycle.
- Apply AI lifecycle concepts in risk discussions or system planning.
- Support alignment of AI system activities with ISO/IEC 42001 and ISO/IEC 22989.
- Enable clear internal communication based on shared conceptual understanding.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of digital technologies and computing systems.
- Familiarity with general system lifecycle concepts and terms.
- Introductory exposure to artificial intelligence or automated systems.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: strong foundational knowledge of AI systems, lifecycle models, taxonomy, and ISO/IEC standards including 42001 and 23894.
- Certifications: relevant qualifications in AI governance, system development, or technical standards such as ISO/IEC 42001, ISO/IEC 22989, and ISO/IEC 23894.
- Training & Practical Experience: at least 2 years of experience in explaining AI architecture, terminology, and standards-based AI lifecycle implementation.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand the core terminology and definitions related to AI according to ISO/IEC 22989.
- Gain insight into the structure, stages, and activities of the AI system lifecycle.
- Learn how AI terminology supports communication, risk identification, and compliance efforts.
- Connect concepts of trustworthy AI to lifecycle design and decision-making processes.
- Develop a common conceptual basis for further education or implementation of AI management systems.
