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Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Major challenges in AI interpretability
Basic XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Model-specific explainability strategies
- Interpreting decisions from black-box models
Practical Use of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into basic machine learning models
- Visualising model behaviour and explanations
Challenges in Explainability
- The balance between accuracy and interpretability
- Constraints of existing XAI methods
- Managing bias and fairness in explainable models
Ethical Implications of XAI
- The ethical consequences of AI transparency
- Aligning explainability with model performance
- Privacy and data security considerations in XAI
Real-World XAI Applications
- XAI in healthcare, finance, and law enforcement
- Regulatory standards for explainability
- Fostering trust in AI systems via transparency
Advanced XAI Concepts
- Counterfactual explanations
- Interpreting neural networks and deep learning models
- Analysing complex AI systems
Future Directions in Explainable AI
- New developments in XAI research
- Future challenges and prospects for AI transparency
- The influence of XAI on responsible AI practices
Requirements
- A foundational understanding of machine learning concepts
- Basic proficiency in Python programming
Target Audience
- Individuals new to AI
- Data science enthusiasts
