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Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Primary challenges in AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to certain models
- Deciphering decisions from black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Applying XAI to basic machine learning models
- Visualizing model behavior and explanations
Obstacles in Explainability
- Trade-offs between accuracy and interpretability
- Constraints of existing XAI methods
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Grasping the ethical consequences of AI transparency
- Striking a balance between explainability and model performance
- Privacy and data security issues in XAI
Practical Uses of XAI
- XAI applications in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Fostering trust in AI systems via transparency
Advanced XAI Principles
- Investigating counterfactual explanations
- Interpreting neural networks and deep learning models
- Making sense of complex AI systems
Emerging Trends in Explainable AI
- New developments in XAI research
- Future challenges and prospects for AI transparency
- The influence of XAI on responsible AI development
Recap and Subsequent Steps
Requirements
- Foundational knowledge of machine learning principles
- Proficiency with Python programming
Target Audience
- Novices in AI
- Data science professionals
14 Hours
