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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency for Government
- Understanding Explainable AI
- The Importance of Transparency in AI Systems
- Balancing Interpretability with Performance in AI Models
Overview of XAI Techniques for Government
- Model-agnostic Methods: SHAP, LIME
- Model-specific Explainability Techniques
- Explaining Neural Networks and Deep Learning Models
Building Transparent AI Models for Government
- Implementing Interpretable Models in Practice
- Comparing Transparent Models with Black-Box Models
- Balancing Model Complexity and Explainability
Advanced XAI Tools and Libraries for Government
- Utilizing SHAP for Model Interpretation
- Leveraging LIME for Local Explainability
- Visualizing Model Decisions and Behaviors
Addressing Fairness, Bias, and Ethical AI in Government
- Identifying and Mitigating Bias in AI Models
- Ensuring Fairness in AI and Its Societal Impacts
- Promoting Accountability and Ethics in AI Deployment
Real-World Applications of XAI for Government
- Case Studies in Healthcare, Finance, and Government
- Interpreting AI Models for Regulatory Compliance
- Building Trust with Transparent AI Systems
Future Directions in Explainable AI for Government
- Emerging Research in XAI
- Challenges in Scaling XAI for Large-Scale Systems
- Opportunities for the Future of Transparent AI
Summary and Next Steps for Government
Requirements
- Experience in machine learning and artificial intelligence model development for government applications
- Proficiency with Python programming
Audience
- Data scientists for government agencies
- Machine learning engineers for government projects
- AI specialists for government initiatives
21 Hours