Advanced Techniques in Explainable AI (XAI)

• Solid understanding of AI and machine learning
• Experience with neural networks and deep learning
• Familiarity with basic XAI techniques

Audience

• Experienced AI researchers
• Machine learning engineers

This course covers the latest advancements in Explainable AI (XAI) techniques, focusing on model transparency and interpretability in complex AI systems. Participants will explore deep learning models, advanced interpretability methods, and their applications in real-world AI systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to enhance their understanding of XAI techniques for complex AI models.

By the end of this training, participants will be able to:

• Implement state-of-the-art XAI techniques in AI models.
• Interpret deep learning models and their decisions.
• Apply advanced model-agnostic and model-specific explainability methods.
• Address challenges related to AI transparency in complex systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Advanced XAI Techniques

• Review of basic XAI methods
• Challenges in interpreting complex AI models
• 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 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