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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 obstacles in AI interpretability
Fundamental XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to model architecture
- Interpreting decisions from black-box models
Practical XAI Tools
- Overview of open-source XAI libraries
- Applying XAI to basic machine learning models
- Visualising model behaviour and explanations
Challenges in Explainability
- Balancing accuracy against interpretability
- Constraints of existing XAI methodologies
- Addressing bias and fairness in explainable models
Ethical Implications of XAI
- Evaluating the ethical impact of AI transparency
- Striking a balance between explainability and model performance
- Data privacy and protection considerations in XAI
Practical Applications of XAI
- Use of XAI in healthcare, finance, and law enforcement
- Regulatory standards regarding explainability
- Establishing trust in AI systems via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning architectures
- Interpreting complex AI systems
Future Directions in Explainable AI
- New developments in XAI research
- Prospects and hurdles for future AI transparency
- The influence of XAI on responsible AI development
Requirements
- Familiarity with core machine learning principles
- Proficiency in Python programming
Target Audience
- Newcomers to AI
- Data science practitioners and enthusiasts
