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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency
- Defining Explainable AI
- The critical role of transparency in AI systems
- Weighing interpretability against performance in AI models
Overview of XAI Techniques
- Model-agnostic approaches: SHAP, LIME
- Explainability techniques specific to certain model types
- Explaining neural networks and deep learning architectures
Building Transparent AI Models
- Practical implementation of interpretable models
- Comparing transparent models against black-box alternatives
- Striking a balance between complexity and explainability
Advanced XAI Tools and Libraries
- Utilising SHAP for model interpretation
- Leveraging LIME for localised explainability
- Visualising model decisions and behavioural patterns
Addressing Fairness, Bias, and Ethical AI
- Identifying and mitigating bias within AI models
- Fairness in AI and its broader societal implications
- 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
- Cultivating trust through transparent AI systems
Future Directions in Explainable AI
- Emerging research trends in XAI
- Challenges associated with scaling XAI for large-scale systems
- Future opportunities for the advancement of transparent AI
Summary and Next Steps
Requirements
- Prior experience in machine learning and AI model development
- Working knowledge of Python programming
Target Audience
- Data scientists
- Machine learning engineers
- AI specialists
21 Hours
