Explainable AI: Making Models Interpretable
Build transparent AI systems. Techniques for explaining model decisions and building trust.
The Need for Explainability
As AI makes important decisions, understanding why becomes crucial for trust, debugging, and compliance.
Levels of Explainability
Global Explanations
Understanding overall model behavior.
Local Explanations
Understanding individual predictions.
Model-Specific
Inherently interpretable models.
Model-Agnostic
Techniques for any model.
Explanation Techniques
Feature Importance
Local Explanations
Visualization
Rule Extraction
Interpretable Models
Inherently Interpretable
Constrained Models
Practical Implementation
Challenges
Tools
Conclusion
Explainable AI builds trust and enables responsible deployment of AI systems.
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