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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 challenges associated with AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to certain models
- Interpreting decisions generated by black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualizing model behavior and explanations
Navigating Explainability Challenges
- Balancing accuracy against interpretability
- Limitations inherent in current XAI methods
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Assessing the ethical impact of AI transparency
- Maintaining equilibrium between explainability and model performance
- Privacy and data protection issues in the context of XAI
Practical Applications of XAI
- Use of XAI in healthcare, finance, and law enforcement
- Regulatory mandates regarding explainability
- Fostering trust in AI systems via transparency
Advanced Concepts in XAI
- Analyzing counterfactual explanations
- Interpreting neural networks and deep learning architectures
- Understanding complex AI systems
Future Directions in Explainable AI
- New developments in XAI research
- Future challenges and opportunities for AI transparency
- The influence of XAI on the evolution of responsible AI
Requirements
- Foundational knowledge of machine learning concepts
- Experience with Python programming
Target Audience
- Beginners in Artificial Intelligence
- Data science enthusiasts
