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Course Outline
Introduction to Advanced XAI Techniques
- Review of foundational XAI methods.
- Challenges 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 explanations.
Model-Specific Explainability Techniques
- Layer-wise relevance propagation (LRP).
- DeepLIFT (Deep Learning Important FeaTures).
- Gradient-based methods (Grad-CAM, Integrated Gradients).
Explaining Deep Learning Models
- Interpreting convolutional neural networks (CNNs).
- Explaining recurrent neural networks (RNNs).
- Analyzing transformer-based models (BERT, GPT).
Handling Interpretability Challenges
- Addressing black-box model limitations.
- Balancing accuracy and interpretability.
- Dealing with bias and fairness in explanations.
Applications of XAI in Real-World Systems
- XAI applications in healthcare, finance, and legal systems.
- AI regulation and compliance requirements.
- Building trust and accountability through XAI.
Future Trends in Explainable AI
- Emerging techniques and tools in XAI.
- Next-generation explainability models.
- Opportunities and challenges in AI transparency.
Summary and Next Steps
Requirements
- Solid understanding of AI and machine learning principles.
- Experience with neural networks and deep learning.
- Familiarity with basic XAI techniques.
Target Audience
- Experienced AI researchers.
- Machine learning engineers.
21 Hours
