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Course Outline
Introduction to Advanced Explainable AI Techniques
- Assessment of foundational explainability methods
- Obstacles in interpreting complex artificial intelligence models
- Current directions in XAI research and development
Model-Agnostic Explainability Approaches
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Anchor-based explanations
Model-Specific Explainability Approaches
- Layer-wise Relevance Propagation (LRP)
- DeepLIFT (Deep Learning Important FeaTures)
- Gradient-based methods (Grad-CAM, Integrated Gradients)
Explaining Deep Learning Architectures
- Interpretation of convolutional neural networks (CNNs)
- Explanation methodologies for recurrent neural networks (RNNs)
- Analysis of transformer-based models (BERT, GPT)
Addressing Interpretability Challenges
- Mitigating limitations inherent in black-box models
- Optimizing the trade-off between predictive accuracy and interpretability
- Managing bias and ensuring fairness within model explanations
Deploying XAI in Operational Systems
- Implementation of XAI within healthcare, financial, and legal sectors
- Adherence to AI regulatory frameworks and compliance mandates for government entities
- Establishing public trust and accountability through transparent AI practices
Future Directions in Explainable AI
- Development of emerging techniques and analytical tools
- Advancement of next-generation explainability frameworks
- Identification of opportunities and challenges regarding AI transparency
Summary and Actionable Next Steps
Requirements
- Comprehensive knowledge of artificial intelligence and machine learning principles
- Practical expertise in neural networks and deep learning frameworks
- Proficiency with foundational Explainable AI (XAI) methodologies
Target Audience
- Seasoned AI research professionals
- Machine learning engineering specialists
21 Hours
