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Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Major challenges in AI interpretability
Fundamental XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to model types
- Interpreting decisions from black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning workflows
- Visualizing model behavior and explanations
Challenges in Explainability
- Trade-offs between accuracy and interpretability
- Constraints of contemporary XAI methods
- Managing bias and fairness in explainable models
Ethical Dimensions of XAI
- Evaluating the ethical implications of AI transparency
- Balancing explainability with model performance
- Addressing privacy and data protection in XAI
Real-World XAI Applications
- XAI in healthcare, finance, and law enforcement
- Compliance with regulatory explainability standards
- Fostering trust in AI systems through transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning architectures
- Interpreting complex AI systems
Future Directions in Explainable AI
- Emerging research trends in XAI
- Future challenges and opportunities for AI transparency
- The role of XAI in the development of responsible AI
Requirements
- Familiarity with foundational machine learning concepts
- Proficiency in Python programming
Target Audience
- Individuals new to AI
- Enthusiasts in data science
