2Explainable Artificial Intelligence (XAI)

1. Table of contents Introduction XAI History of XAI Difference Between AI and XAI COMPUTER REPAIR & SUPPORT

2. Table of contents XAI Grown Importance of XAI Applications of XAI XAI Tools and Techniques COMPUTER REPAIR & SUPPORT

3. Introduction Explainable AI (XAI) refers to a set of processes and methods that allow human users to comprehend and trust the results and output created by machine learning algorithms. XAI seeks to make the decision-making process of AI models transparent, interpretable, and understandable.

4. XAI Stands for: Explainable Artificial Intelligence Focuses on making AI models understandable to humans Answers questions like: Why did the model make this prediction? Which features were most influential? How confident is the model in its output?

5. History of XAI 1980s: Emerged in expert systems; explanation was simpler due to rule-based logic. 2010s: Deep learning widespread, causing concern due to their black- box nature. 2016: DARPA launched the XAI program to promote transparency inAI systems. Today: XAI is a core part of Responsible AI and ethical machine learning. models became

6. Difference Between AI and XAI Aspect Goal AI XAI Optimize accuracy Improve transparency and interpretability Model type Often black-box Post-hoc explanation or inherently interpretable Audience Machines, developers Humans, regulators, domain experts Key Question What is the output? Why is this the output?

7. XAI Grown 1. Trust: Users need to understand decisions to trustAI. 2. Legal Requirements: GDPR and similar laws require explainability. 3. Debugging and Optimization: Helps developers identify flaws. 4. Fairness and Bias Detection: Prevents discrimination in sensitive domains.

8. Importance of XAI • Enables ethical and responsibleAI • Ensures accountability and transparency • Critical in high-stakes fields: - Healthcare - Finance - Criminal Justice -Autonomous Systems

9. Applications of XAI Sector Application Example Healthcare Explaining disease risk predictions Understanding loan approval/rejection Explaining autonomous vehicle actions Justifying automated surveillance alerts Clarifying why a product or movie was suggested Finance Automotive Security Recommendations

10. XAI Tools and Techniques Post-hoc Explanation Tools: - SHAP: Uses game theory to assign feature importance - LIME: Creates simple, local surrogate models for explanation - Grad-CAM: Highlights important regions in images (vision models) Interpretable Models: - Decision Trees - Logistic Regression - Rule-based Systems