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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency
- What constitutes Explainable AI?
- The significance of transparency in AI systems
- The trade-off between interpretability and performance in AI models
Overview of XAI Techniques
- Model-agnostic methods: SHAP and LIME
- Explainability techniques specific to certain models
- Interpreting neural networks and deep learning models
Building Transparent AI Models
- Practical implementation of interpretable models
- Comparing transparent models against black-box models
- Balancing model complexity with the need for explainability
Advanced XAI Tools and Libraries
- Utilising SHAP for model interpretation
- Leveraging LIME for local explainability
- Visualising model decisions and behaviours
Addressing Fairness, Bias, and Ethical AI
- Identifying and mitigating bias within AI models
- Fairness in AI and its broader societal impacts
- Ensuring accountability and ethical standards in AI deployment
Real-World Applications of XAI
- Case studies from healthcare, finance, and government sectors
- Interpreting AI models for regulatory compliance
- Building trust through the use of transparent AI systems
Future Directions in Explainable AI
- Emerging research trends in XAI
- Challenges associated with scaling XAI for large-scale systems
- Opportunities for the future of transparent AI
Summary and Next Steps
Requirements
- Experience in machine learning and the development of AI models
- Familiarity with Python programming
Audience
- Data scientists
- Machine learning engineers
- AI specialists
21 Hours
