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Duration 14 hours
Course Outline
Foundations of Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Primary challenges in AI interpretability
Core XAI Methodologies
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to certain models
- Clarifying decisions from black-box models
Practical XAI Tool Usage
- Overview of open-source XAI libraries
- Applying XAI to simple machine learning models
- Visualizing model behavior and explanations
Explainability Challenges
- Balancing accuracy and interpretability
- Constraints of existing XAI methods
- Addressing bias and fairness in explainable models
Ethics in XAI
- Evaluating the ethical dimensions of AI transparency
- Balancing explainability with model performance
- Data privacy and protection issues in XAI
Practical XAI Applications
- Use of XAI in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Fostering trust in AI systems via transparency
Advanced XAI Topics
- Investigating counterfactual explanations
- Interpreting neural networks and deep learning models
- Analyzing complex AI systems
Future Directions in XAI
- Developing techniques in XAI research
- Future hurdles and prospects for AI transparency
- The role of XAI in responsible AI development
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in Python programming
Target Audience
- Novices in AI
- Data science enthusiasts
