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Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The significance of transparency in AI models
- Primary challenges in AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Model-specific explainability methods
- Clarifying decisions from black-box models
Practical Work with XAI Tools
- Overview of open-source XAI libraries
- Implementing XAI in basic machine learning models
- Visualizing explanations and model behavior
Challenges in Explainability
- Balancing accuracy with interpretability
- Current limitations of XAI methods
- Addressing bias and fairness in explainable models
Ethical Considerations in XAI
- Grasping the ethical implications of AI transparency
- Weighing explainability against model performance
- Privacy and data protection issues in XAI
Real-World Applications of XAI
- XAI usage in healthcare, finance, and law enforcement
- Regulatory standards for explainability
- Establishing trust in AI systems via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning models
- Interpreting complex AI systems
Future Trends in Explainable AI
- Emerging techniques in XAI research
- Challenges and opportunities for future AI transparency
- The influence of XAI on responsible AI development
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning concepts
- Proficiency in Python programming
Target Audience
- Novices in AI
- Data science enthusiasts
14 Hours
