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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency
- What is Explainable AI?
- Why transparency matters in AI systems.
- The trade-off between interpretability and 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.
Target Audience
- Data scientists.
- Machine learning engineers.
- AI specialists.
21 Hours
