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Starts 7 June 2025 20:18
Ends 7 June 2025
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Overview
Explore essential principles and practices for identifying, addressing, and preventing AI bias while understanding the crucial role of AI governance in developing fair systems.
Syllabus
- Introduction to AI Bias
- Identifying AI Bias
- Addressing AI Bias
- Preventing AI Bias
- AI Governance and Ethical Considerations
- Case Studies and Applications
- Workshop and Group Discussions
- Course Review and Future Considerations
Definition and types of AI bias
Historical context and examples
Impact of AI bias on society
Data collection and preprocessing
Bias detection methods
Tools and techniques for bias identification
Techniques for bias mitigation
Fairness-aware algorithms
Case studies of bias correction
Best practices in AI system design
Inclusive data collection and model training
Continuous monitoring and evaluation
Principles of AI ethics and fairness
Regulatory frameworks and compliance
Role of transparency and accountability
Real-world examples of AI bias in various industries
Lessons learned from past implementations
Hands-on bias detection and correction exercises
Group discussions on ethical dilemmas and governance policies
Summary of key concepts
Emerging trends in AI fairness and governance
Subjects
Data Science
