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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 and SHAP
- Model-specific explainability strategies
- Interpreting decisions from black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into basic machine learning models
- Visualization of explanations and model behavior
Navigating Explainability Challenges
- Managing the trade-off between accuracy and interpretability
- Limitations of existing XAI methods
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Ethical implications of AI transparency
- Balancing explainability against model performance
- Privacy and data protection issues in XAI
XAI in Real-World Contexts
- Applications in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Establishing trust in AI systems via transparency
Advanced Concepts in XAI
- Counterfactual explanations
- Interpreting neural networks and deep learning models
- Analyzing complex AI systems
Emerging Trends in Explainable AI
- Latest developments in XAI research
- Future challenges and opportunities for AI transparency
- The influence of XAI on responsible AI advancement
Requirements
- A foundational grasp of machine learning principles
- Proficiency in Python programming
Target Audience
- Novices in AI
- Data science enthusiasts
