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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 ensuring AI interpretability
Core XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods tailored to specific model types
- Deciphering decisions made by black-box models
Practical Application with XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualizing explanations and understanding model behavior
Navigating Explainability Challenges
- Balancing accuracy with interpretability
- Recognizing the limitations of current XAI methods
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Understanding the ethical implications of AI transparency
- Harmonizing explainability with model performance
- Privacy and data protection issues in XAI
Real-World XAI Applications
- Utilizing XAI in healthcare, finance, and law enforcement
- Regulatory standards for explainability
- Fostering trust in AI systems through transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Clarifying neural networks and deep learning models
- Interpreting complex AI systems
Future Directions in Explainable AI
- Emerging trends in XAI research
- Challenges and opportunities for future AI transparency
- The influence of XAI on responsible AI development
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts
- Proficiency in Python programming
Audience
- Individuals new to AI
- Enthusiasts of data science
14 Hours
