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Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI).
- The role of transparency in AI models.
- Primary challenges in AI interpretability.
Basic XAI Techniques
- Model-agnostic approaches using LIME and SHAP.
- Explainability methods specific to certain model types.
- Interpreting decisions from black-box models.
Hands-On with XAI Tools
- Overview of open-source XAI libraries.
- Applying XAI to simple machine learning models.
- Visualizing model explanations and behavior.
Challenges in Explainability
- Navigating the trade-offs between accuracy and interpretability.
- Current limitations of XAI methodologies.
- Addressing bias and ensuring fairness in explainable models.
Ethical Considerations in XAI
- Evaluating the ethical implications of AI transparency.
- Balancing explainability against model performance.
- Addressing privacy and data protection issues in XAI.
Real-World Applications of XAI
- Application of XAI in healthcare, finance, and law enforcement.
- Compliance with regulatory standards for explainability.
- Fostering trust in AI systems through transparency.
Advanced XAI Concepts
- Investigating counterfactual explanations.
- Explaining neural networks and deep learning architectures.
- Interpreting complex AI ecosystems.
Future Trends in Explainable AI
- Emerging methodologies in XAI research.
- Opportunities and hurdles in future AI transparency.
- The influence of XAI on responsible AI development.
Requirements
- A foundational understanding of machine learning concepts.
- Working knowledge of Python programming.
Target Audience
- AI beginners.
- Data science enthusiasts.
