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Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Primary challenges in AI interpretability
Basic XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to certain models
- Interpreting decisions from black-box models
Hands-On with XAI Tools
- Overview of open-source XAI libraries
- Implementing XAI in elementary machine learning models
- Visualizing model explanations and behavior
Challenges in Explainability
- Trade-offs between accuracy and interpretability
- Constraints of existing XAI methods
- Addressing bias and fairness in explainable models
Ethical Considerations in XAI
- Navigating the ethical implications of AI transparency
- Striking a balance between explainability and model performance
- Privacy and data protection issues in XAI
Real-World Applications of XAI
- Applications of XAI in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Fostering trust in AI systems through transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning models
- Interpreting intricate AI systems
Future Trends in Explainable AI
- Nascent techniques in XAI research
- Future challenges and opportunities for AI transparency
- The influence of XAI on responsible AI development
Requirements
- A foundational grasp of machine learning concepts
- Proficiency in Python programming
Target Audience
- Individuals new to AI
- Data science enthusiasts
