This interactive, tutor-led online training course is designed to explore the critical ethical considerations surrounding Artificial Intelligence (AI) in today’s rapidly evolving technology landscape.
Participants will gain a foundational understanding of AI ethics, delve into real-world cases, and develop practical strategies to identify and mitigate ethical risks in AI systems. Through discussions, hands-on activities, and collaborative learning, this course equips professionals with the tools to ensure ethical AI development and deployment.
Course Modules
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Introduction to AI Ethics:
Definition and importance of AI ethics (ethics vs. compliance vs. morals).
• The societal impact of AI and the need for public trust.
• Overview of key ethical challenges: bias, transparency, privacy, and
accountability.
• Activities: Icebreaker introductions and agenda walkthrough. -
Core Ethical Principles & Frameworks :
Fairness, accountability, transparency, privacy, and safety in AI.
• Introduction to AI ethics frameworks (IEEE, UNESCO, EU Commission).
• Activities: Knowledge check via quiz and visual examples.
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Real-World Cases & Impacts:
Examples of biased AI systems and their societal consequences.
• The role of public opinion, media, and regulation in ethical AI use.
• Activities: Case study on a biased loan application AI, with group discussions on mitigation strategies. -
Mitigating Bias & Hands-On Mini-Demo :
Techniques for detecting and addressing bias in training data and models.
• Demonstration of bias checks and mitigation techniques using a Jupyter notebook.
• Activities: Practical walkthrough of model fairness and rebalancing methods. -
Regulatory Landscape & Governance:
Overview of key AI regulations (GDPR, EU AI Act proposals, U.S. guidelines).
• Internal governance processes: ethics committees, audits, and crossfunctional teams.
• Activities: Group reflection on governance gaps and scenario-based discussions. -
Breakout Activity: Ethical Risk Assessment :
Structured approach to identifying and mitigating ethical risks.
• Stakeholder analysis and mitigation strategy development.
• Activities: Group exercise on assessing risks in a public school AI system. -
Quiz & Reflection:
Recap of ethical principles, real-world cases, and mitigation strategies.
• Personal reflection on lessons learned
