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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency
- What is Explainable AI?
- Why transparency matters in AI systems
- Interpretability vs. performance in AI models
Overview of XAI Techniques
- Model-agnostic methods: SHAP, LIME
- Model-specific explainability techniques
- Explaining neural networks and deep learning models
Building Transparent AI Models
- Implementing interpretable models in practice
- Comparing transparent models vs. black-box models
- Balancing complexity with explainability
Advanced XAI Tools and Libraries
- Using SHAP for model interpretation
- Leveraging LIME for local explainability
- Visualization of model decisions and behaviors
Addressing Fairness, Bias, and Ethical AI
- Identifying and mitigating bias in AI models
- Fairness in AI and its societal impacts
- Ensuring accountability and ethics in AI deployment
Real-World Applications of XAI
- Case studies in healthcare, finance, and government
- Interpreting AI models for regulatory compliance
- Building trust with transparent AI systems
Future Directions in Explainable AI
- Emerging research in XAI
- Challenges in scaling XAI for large-scale systems
- Opportunities for the future of transparent AI
Summary and Next Steps
Requirements
- Experience in machine learning and AI model development
- Familiarity with Python programming
Audience
- Data scientists
- Machine learning engineers
- AI specialists
21 Hours
