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Course Outline
Foundations of Explainable AI (XAI) and Model Transparency
- Defining the scope of Explainable AI
- The necessity of transparency in AI ecosystems
- Weighing interpretability against performance metrics
Survey of XAI Methodologies
- Model-agnostic approaches: SHAP and LIME
- Techniques for explaining neural networks and deep learning architectures
Constructing Transparent AI Models
- Practical implementation of interpretable models
- Finding the balance between complexity and explainability
Advanced XAI Tools and Libraries
- Applying SHAP for detailed model analysis
- Visualizing decision processes and model behaviors
Fairness, Bias Mitigation, and Ethical AI
- Detecting and reducing bias in AI models
Real-World XAI Applications
- Establishing trust through transparent AI systems
Future Trajectories in Explainable AI
- Current trends in XAI research
Recap and Future Pathways
Requirements
- Practical experience in machine learning and AI model construction
- Proficiency in Python programming
Target Audience
- Data Scientists
- Machine Learning Engineers
- AI Specialists
21 Hours
