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1. Explainable AI (XAI)
• Understanding, Trusting, and Interpreting AI
Systems

2. What is Explainable AI?
• XAI refers to methods that make AI decisions
transparent and understandable.

3. Why XAI is Important
• • Builds trust
• • Ensures fairness
• • Improves accountability
• • Helps debugging models

4. Types of Explanations
• • Global explanations
• • Local explanations
• • Model-specific
• • Model-agnostic

5. Techniques in XAI
• • LIME
• • SHAP
• • Decision Trees
• • Counterfactual Explanations

6. LIME
• Local interpretable model-agnostic
explanations
• Explains predictions by approximating locally
with simpler models.

7. SHAP
• SHapley Additive exPlanations
• Based on cooperative game theory to assign
feature importance.

8. XAI in Healthcare
• • Interpreting diagnostic models
• • Explaining risk predictions
• • Enhancing clinician trust

9. Challenges in XAI
• • Complexity
• • Trade-off between accuracy &
interpretability
• • Lack of standard metrics

10. Conclusion
• XAI is essential for safe, fair, and trustworthy
AI systems.