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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency
- Defining Explainable AI.
- The significance of transparency in AI systems.
- Weighing interpretability against performance in AI models.
Survey of XAI Methodologies
- Model-agnostic approaches: SHAP and LIME.
- Explainability techniques specific to certain models.
- Interpreting neural networks and deep learning architectures.
Developing Transparent AI Models
- Implementing interpretable models in practical settings.
- Evaluating transparent models against black-box alternatives.
- Striking a balance between model complexity and explainability.
Utilizing Advanced XAI Tools and Libraries
- Applying SHAP for detailed model interpretation.
- Using LIME to achieve local explainability.
- Visualizing model decision-making processes and behaviors.
Mitigating Fairness, Bias, and Promoting Ethical AI
- Detecting and reducing bias in AI models.
- Understanding AI fairness and its broader societal implications.
- Guaranteeing accountability and ethical standards in AI deployment.
Practical Applications of XAI
- Case studies from the healthcare, finance, and government sectors.
- Interpreting AI models to meet regulatory compliance requirements.
- Fostering trust through transparent AI systems.
Emerging Trends in Explainable AI
- Current research developments in XAI.
- Challenges associated with scaling XAI for large-scale systems.
- Future prospects for transparent AI.
Key Takeaways and Subsequent Steps
Requirements
- Practical experience in machine learning and developing AI models.
- Proficiency in Python programming.
Target Audience
- Data scientists.
- Machine learning engineers.
- AI specialists.
21 Hours
