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Duration 14 hours
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 strategies
- Interpreting decisions generated by black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualizing model explanations and behavior
Challenges in Explainability
- Trade-offs between accuracy and interpretability
- Current limitations of XAI methodologies
- Managing bias and fairness in explainable models
Ethical Dimensions of XAI
- Analyzing the ethical impacts of AI transparency
- Striking a balance between explainability and model performance
- Addressing privacy and data protection issues in XAI
Real-World Applications of XAI
- XAI applications in healthcare, finance, and law enforcement
- Compliance with regulatory explainability requirements
- Fostering trust in AI systems via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning models
- Interpreting complex AI systems
Future Directions in Explainable AI
- Emerging techniques in XAI research
- Future challenges and opportunities for AI transparency
- The influence of XAI on responsible AI development
Requirements
- A foundational understanding of machine learning concepts
- Proficiency with Python programming
Target Audience
- Beginners in AI
- Data science enthusiasts
