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Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The significance of transparency in AI models
- Major challenges regarding AI interpretability
Basic XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Model-specific explainability strategies
- Clarifying decisions from black-box models
Hands-On with XAI Tools
- Overview of open-source XAI libraries
- Implementing XAI in simple machine learning models
- Visualizing model behavior and explanations
Challenges in Explainability
- The trade-off between accuracy and interpretability
- Limitations of current XAI methodologies
- Addressing bias and fairness in explainable models
Ethical Considerations in XAI
- Comprehending the ethical impact of AI transparency
- Balancing explainability against model performance
- Privacy and data protection issues in XAI
Real-World Applications of XAI
- XAI use cases in healthcare, finance, and law enforcement
- Regulatory standards for explainability
- Fostering trust in AI systems via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Interpreting neural networks and deep learning models
- Analyzing complex AI systems
Future Trends in Explainable AI
- Developing techniques in XAI research
- Future challenges and opportunities in AI transparency
- The influence of XAI on responsible AI development
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in Python programming
Target Audience
- AI novices
- Data science enthusiasts
