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Course Outline
Introduction to Advanced XAI Techniques
- Recap of fundamental XAI methods
- Obstacles in interpreting complex AI models
- Current trends in XAI research and development
Model-Agnostic Explainability Techniques
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Anchor-based explanations
Model-Specific Explainability Techniques
- Layer-wise relevance propagation (LRP)
- DeepLIFT (Deep Learning Important FeaTures)
- Gradient-based approaches (Grad-CAM, Integrated Gradients)
Explaining Deep Learning Models
- Interpreting convolutional neural networks (CNNs)
- Deciphering recurrent neural networks (RNNs)
- Analysis of transformer-based models (BERT, GPT)
Handling Interpretability Challenges
- Mitigating the limitations of black-box models
- Achieving a balance between accuracy and interpretability
- Addressing bias and fairness within explanations
Applications of XAI in Real-World Systems
- Deploying XAI in healthcare, finance, and legal domains
- Navigating AI regulation and compliance standards
- Fostering trust and accountability via XAI
Future Trends in Explainable AI
- Novel techniques and tools emerging in XAI
- Next-generation explainability models
- Prospects and hurdles in achieving AI transparency
Summary and Next Steps
Requirements
- A robust command of AI and machine learning principles
- Practical experience with neural networks and deep learning
- Basic knowledge of foundational XAI techniques
Target Audience
- Senior AI researchers
- Machine learning engineers
21 Hours
