Introduction
As AI becomes more influential in society, ethical considerations are fundamental to trustworthy adoption. This course explores fairness, transparency, accountability, and governance of AI systems. Participants will understand key risks posed by biased data, misaligned objectives, and opaque models. Through case studies, learners will evaluate real-world ethical dilemmas and examine regulatory frameworks. The course equips professionals with tools to design and assess responsible AI solutions.
Course Objectives
- Understand major ethical concerns in AI
- Learn frameworks for evaluating AI risk
- Explore fairness and bias mitigation
- Study global AI regulations and guidelines
- Build responsible AI practices
Target Audience
- Policymakers
- Business leaders and managers
- AI/ML engineers
- Researchers and academics
- Professionals deploying AI systems
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: Introduction to AI Ethics• Why ethics matters
• Key ethical principles
• Historical examples
• Terminology
• Risks from AI misuse0 - Day 2: Fairness & Bias• Types of bias
• Sources of bias
• Detection methods
• Mitigation approaches
• Case study0 - Day 3: Transparency & Explainability• Explainable AI techniques
• Model interpretability tools
• Black-box models
• Trade-offs in transparency
• Hands-on: Model interpretation0 - Day 4: Accountability & Governance• Organizational responsibility
• Ethical review processes
• AI auditing
• Global regulatory trends
• Risk frameworks0 - Day 5: Building Responsible AI• Ethical design patterns
• Documentation best practices
• Monitoring deployed systems
• Ethics in generative AI
• Workshop: Create an AI ethics plan0
Instructor
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