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Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI).
- The significance of transparency in AI models.
- Key obstacles in achieving AI interpretability.
Foundational XAI Techniques
- Model-agnostic approaches: LIME, SHAP.
- Explainability methods specific to certain models.
- Deciphering decisions made by black-box models.
Practical XAI Tools
- Overview of open-source XAI libraries.
- Implementing XAI in basic machine learning contexts.
- Visualizing model explanations and behavior.
Challenges in Explainability
- Balancing accuracy with interpretability.
- Current limitations of XAI methodologies.
- Addressing bias and fairness in explainable models.
Ethical Considerations in XAI
- Examining 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
- 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.
- Explaining neural networks and deep learning models.
- Interpreting complex AI systems.
Future Trends in Explainable AI
- Emerging techniques 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 concepts.
- Proficiency in Python programming.
Audience
- Individuals new to AI.
- Enthusiasts of data science.
14 Hours
