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Duration 14 hours
Course Outline
Overview of Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI modeling
- Primary challenges related to AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches, such as LIME and SHAP
- Explainability methods specific to certain models
- Interpreting decisions generated by black-box models
Practical Application of XAI Tools
- Introduction to open-source XAI libraries
- Applying XAI to simple machine learning models
- Visualizing model behavior and explanations
Navigating Explainability Challenges
- Balancing accuracy against interpretability
- Current limitations of XAI methodologies
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Assessing the ethical impact of AI transparency
- Striking a balance between explainability and performance
- Managing privacy and data protection issues within XAI
Real-World XAI Implementations
- Applications in healthcare, finance, and law enforcement
- Compliance with regulatory explainability standards
- Fostering trust in AI systems through transparency
Advanced Concepts in XAI
- Analyzing counterfactual explanations
- Interpreting neural networks and deep learning architectures
- Making sense of complex AI systems
Future Directions in Explainable AI
- New developments in XAI research
- Future challenges and opportunities for AI transparency
- The role of XAI in advancing responsible AI practices
Requirements
- A foundational understanding of machine learning principles
- Working proficiency in Python programming
Target Audience
- Individuals new to AI
- Data science professionals and enthusiasts
