XAI

May 07, 2025

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COMPUTER REPAIR & SUPPORT Table of contents Introduction XAI History of XAI Difference Between AI and XAICOMPUTER REPAIR & SUPPORT Table of contents XAI Grown Importance of XAI Applications of XAI XAI Tools and TechniquesIntroduction 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.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?History of XAI 1980s: Emerged in expert systems; explanation was simpler due to rule-based logic. 2010s: Deep learning models became widespread, causing concern due to their blackbox nature. 2016: DARPA launched the XAI program to promote transparency in AI systems. Today: XAI is a core part of Responsible AI and ethical machine learning.Difference Between AI and XAI Aspect AI XAI Goal 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?XAI Grown 1. Trust: Users need to understand decisions to trust AI. 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.Importance of XAI u2022 Enables ethical and responsible AI u2022 Ensures accountability and transparency u2022 Critical in high-stakes fields: - Healthcare - Finance - Criminal Justice - Autonomous SystemsApplications of XAI Sector Application Example Healthcare Explaining disease risk predictions Finance Understanding loan approval/rejection Automotive Explaining autonomous vehicle actions Security Justifying automated surveillance alerts Recommendations Clarifying why a product or movie was suggestedXAI 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

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XAI

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