Ethics in AI

. 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.
2. 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.
3. 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.
4. 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.
5. Regulatory Landscape & Governance
• Overview of key AI regulations (GDPR, EU AI Act proposals, U.S. guidelines).
• Internal governance processes: ethics committees, audits, and cross
functional teams.
• Activities: Group reflection on governance gaps and scenario-based
discussions.
6. 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.
7. Quiz & Reflection
• Recap of ethical principles, real-world cases, and mitigation strategies.
• Personal reflection on lessons learned.

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