Explainable_Artificial intelligence in engineering

Explanation about explainable AI

1. Explainable AI (XAI) and Interpretability Challenges Seminar Presentation Your Name | Roll Number | Institution

2. Introduction AI models are often 'black boxes' – difficult to understand Explainable AI (XAI) = techniques to make AI decisions understandable Goal: Improve trust, transparency, and adoption

3. Why Do We Need XAI? Trust in AI predictions and decisions Accountability (e.g., GDPR compliance) Debugging and bias detection in models Encourages adoption of AI systems

4. XAI Approaches Model-Specific: Decision Trees, Attention Mechanisms Model-Agnostic: LIME, SHAP, Counterfactual Explanations

5. LIME (Local Interpretable Model-Agnostic Explanations) Explains individual predictions locally Approximates complex model with a simple interpretable one Useful for understanding black-box models

6. SHAP (SHapley Additive exPlanations) Based on game theory (Shapley values) Shows contribution of each feature Provides global + local interpretability

7. Counterfactual Explanations Answer 'what if' questions Show how small input changes can alter output Helpful in fairness and decision justification

8. Interpretability Challenges High complexity of deep learning models Trade-off: accuracy vs interpretability Bias and fairness issues in datasets Different stakeholders need different explanations Real-time scalability issues

9. Applications of XAI Healthcare: Diagnosis explanations Finance: Loan approval, fraud detection Autonomous Vehicles: Explaining driving actions Cybersecurity: Intrusion detection explanation

10. XAI in Healthcare AI predicts disease from X-rays/CT scans XAI highlights regions responsible for predictions Helps doctors trust AI decisions

11. XAI in Finance Loan approval explanations Fraud detection reasoning Improves fairness and accountability

12. Future of XAI Human-centered explanations Compliance with AI regulations (EU AI Act, GDPR) Interactive XAI (ask 'why/why not' questions) Combining ethics with explainability

13. Benefits of XAI Builds user trust in AI systems Encourages adoption in sensitive domains Improves debugging and model reliability Supports legal and ethical AI usage

14. Summary XAI = bridge between AI accuracy and human trust Techniques: LIME, SHAP, Counterfactuals Challenges: complexity, bias, scalability Future: interactive, ethical, and human-centered AI

15. Q&A Thank you! Questions are welcome.