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Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The critical role of transparency in AI models
- Major challenges in AI interpretability
Core XAI Techniques
- Model-agnostic methods: LIME, SHAP
- Explainability methods specific to certain models
- Clarifying decisions made by black-box models
Practical Application with XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into basic machine learning models
- Visualizing explanations and model behavior
Addressing Challenges in Explainability
- Balancing accuracy against interpretability
- Limitations of existing XAI methods
- Managing bias and ensuring fairness in explainable models
Ethical Dimensions of XAI
- Recognizing the ethical implications of AI transparency
- Striking a balance between explainability and model performance
- Addressing privacy and data protection issues in XAI
Real-World Applications of XAI
- Utilizing XAI in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Building trust in AI systems through transparency
Advanced Concepts in XAI
- Investigating counterfactual explanations
- Explaining neural networks and deep learning architectures
- Interpreting complex AI systems
Future Trends in Explainable AI
- New developments in XAI research
- Challenges and opportunities for future AI transparency
- The impact of XAI on responsible AI development
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in Python programming
Target Audience
- Beginners in the field of AI
- Enthusiasts of data science
14 Hours
