- Angel Draev
- Ghazi Mabrouk
Introductory
Event Organizer(s)
Funded by
Description
This online self-paced course introduces participants to the fundamentals of Artificial Intelligence (AI) governance through five structured modules. Each module blends clear explanations of key AI concepts with practical, real-world scenarios to illustrate how AI impacts daily life, public services, and global policy. Learners will explore topics ranging from the basics of AI to issues of bias, transparency, data governance, regulatory models, and ethics (fairness, inclusivity, transparency, accountability, human oversight). Designed for non-technical audiences, the course equips policymakers, professionals, and civil society actors with the knowledge needed to critically assess AI systems and engage in governance discussions.
Each module can be undertaken separately with an ITU Academy badge awarded for successful completion. Completing all modules and the final test will bring additional ITU Academy badge.
Designed for non-technical participants, this course is ideal for policymakers, regulators, business professionals, civil society organizations, international organizations’ staff, and students looking to understand how AI is shaping societies and economies worldwide. It breaks down complex AI concepts into clear, accessible language, making it easy for participants to grasp the implications of AI without requiring a technical background.
This introductory course provides an overview of AI governance and does not require a specialized background in the subject. However, a keen interest in AI-related legal, regulatory, or policy frameworks is recommended.
Recommended Background
While no formal qualifications are required, participants will benefit from having:
- Professional experience in governance, technology policy, AI development, data protection, or cybersecurity (desirable but not mandatory).
- Familiarity with AI-related regulations or policies or experience in sectors utilizing AI technologies (e.g., healthcare, finance, or digital infrastructure).
- Proficiency in English, as all course materials and assessments will be conducted in English.
By completing the course, participants will be able to:
- Understand AI foundations: Define AI, distinguish it from traditional computing, and explain the different types of AI (ANI, AGI, and Generative AI).
- Identify AI use cases: Recognize how AI is applied across sectors such as health, finance, education, and public services.
- Assess AI challenges: Explain how bias, lack of transparency, and accountability gaps create risks for fairness and trust.
- Understand data governance in AI development and deployment: Outline the principles of data ownership, consent, quality, and security, and compare global approaches to data protection.
- Understand AI governance models: Compare risk-based and rights-based regulatory frameworks and assess international, regional, and national approaches to AI regulation.
- Apply AI ethical principles: Reflect on fairness, transparency, accountability, inclusivity, and human oversight in real-world AI applications.
The course applies an interactive, learner-centered approach tailored to an online, self-paced format. Participants will progress through a mix of learning tools designed to build knowledge, encourage reflection, and link theory with practice.
- Structured Learning Modules: Engaging video lectures, interactive readings, and factsheets introduce and explain core concepts.
- Case-Based Scenarios: Story-driven examples ground abstract principles in real-world dilemmas, helping learners analyze governance challenges.
- Knowledge Checks: Quizzes and quick assessments after each module reinforce understanding, provide instant feedback, and guide learners toward mastery.
- Wrap-Up and Resources: The course concludes with a synthesis of key lessons and curated references for continued learning on AI governance and ethics.
This self-paced course employs a balanced and flexible assessment strategy designed to measure both participants’ comprehension of fundamental AI governance concepts and participants’ ability to apply this knowledge in real-world and professional contexts.
Module completion: Multiple-choice questions quiz at the end of each module to reinforce key concepts:
Passing Criteria
- A total score of 80% or higher is required to obtain the ITU badge attesting completion for each module.
- Participants must complete all required assignments and quizzes.
Overall course completion: Final multiple-choice questions quiz after completion of all modules:
Passing Criteria
- Participants must complete all five modules to unlock the final quiz.
- A total score of 80% is required to obtain the ITU badge attesting completion of the overall course.
Module 1 – Introduction to AI Governance - 35 mins (approx.)
By the end of this module, you will be able to:
- Define AI and distinguish it from traditional computing.
- Differentiate among ANI, AGI, and Generative AI.
- Identify AI use cases across different sectors.
- Explain why governance is necessary to ensure that AI serves society fairly.
Module 2 – Bias in AI and Accountability Mechanisms - 35 mins (approx.)
By the end of this module, you will be able to:
- Explain how bias in data, model design, and deployment practices can lead to discriminatory or unfair outcomes in automated decision-making.
- Identify transparency and explainability challenges in so-called 'black box' AI systems and understand their implications for regulatory oversight.
- Identify accountability and redress mechanisms for AI systems that cause harm, including the roles of regulators, system developers, deployers, and public authorities.
By the end of this module, you will be able to:
- Explain why data is the foundation of AI systems.
- Identify principles of data governance: ownership, consent, transparency, quality, and security.
- Compare global approaches to data protection and their impact on AI governance.
- Assess best practices for balancing data use with individual rights.
By the end of this module, you will be able to:
- Compare risk-based and rights-based approaches to AI regulation.
- Identify national, regional, and global AI governance frameworks.
- Explain innovative models of governance, including sandboxes and algorithm audits.
- Assess the role of public–private partnerships (PPPs) in shaping AI regulation.
By the end of this module, you will be able to:
- Understand ethical principles (fairness, transparency, accountability, inclusivity, and human oversight) in AI deployment and use.
- Recognize risks of unethical AI deployment.
- Evaluate how public-private partnerships (PPPs) and multi-stakeholder approaches support ethical adoption.
- Reflect on ethical dilemmas in AI development and deployment in the public sector.
Registration information
Unless specified otherwise, all ITU Academy training courses are open to all interested professionals, irrespective of their race, ethnicity, age, gender, religion, economic status and other diverse backgrounds. We strongly encourage registrations from female participants, and participants from developing countries. This includes least developed countries, small island developing states and landlocked developing countries.
