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Computer Science
Artificial Intelligence
Explainable Artificial Intelligence
1. Foundations of Explainable AI
2. Taxonomy and Classification of XAI Methods
3. Intrinsically Interpretable Models
4. Post-hoc Explanation Methods
5. Deep Learning Specific Explanation Methods
6. Tree Ensemble Specific Methods
7. Evaluation of Explanations
8. Practical Applications of XAI
9. Challenges and Limitations
10. Future Directions and Emerging Trends
8.
Practical Applications of XAI
8.1.
Healthcare and Medical AI
8.1.1.
Medical Diagnosis Systems
8.1.1.1.
Radiology Image Analysis
8.1.1.2.
Pathology Slide Interpretation
8.1.1.3.
Clinical Decision Support
8.1.2.
Treatment Recommendation Systems
8.1.2.1.
Personalized Medicine
8.1.2.2.
Drug Dosage Optimization
8.1.2.3.
Treatment Outcome Prediction
8.1.3.
Medical Research Applications
8.1.3.1.
Biomarker Discovery
8.1.3.2.
Clinical Trial Analysis
8.1.3.3.
Epidemiological Studies
8.1.4.
Regulatory Considerations
8.1.4.1.
FDA Approval Requirements
8.1.4.2.
Clinical Validation Standards
8.1.4.3.
Patient Safety Protocols
8.2.
Financial Services
8.2.1.
Credit Risk Assessment
8.2.1.1.
Loan Approval Decisions
8.2.1.2.
Credit Scoring Models
8.2.1.3.
Default Risk Prediction
8.2.2.
Fraud Detection Systems
8.2.2.1.
Transaction Anomaly Detection
8.2.2.2.
Pattern Recognition
8.2.2.3.
False Positive Reduction
8.2.3.
Algorithmic Trading
8.2.3.1.
Investment Decision Support
8.2.3.2.
Risk Management
8.2.3.3.
Market Analysis
8.2.4.
Regulatory Compliance
8.2.4.1.
Fair Lending Practices
8.2.4.2.
Anti-Discrimination Laws
8.2.4.3.
Audit Trail Requirements
8.3.
Autonomous Systems and Robotics
8.3.1.
Self-Driving Vehicles
8.3.1.1.
Perception System Explanations
8.3.1.2.
Decision Justification
8.3.1.3.
Safety Critical Decisions
8.3.2.
Industrial Robotics
8.3.2.1.
Manufacturing Process Optimization
8.3.2.2.
Quality Control Systems
8.3.2.3.
Predictive Maintenance
8.3.3.
Human-Robot Interaction
8.3.3.1.
Action Explanation
8.3.3.2.
Intent Communication
8.3.3.3.
Trust Building
8.4.
Legal and Criminal Justice
8.4.1.
Risk Assessment Tools
8.4.1.1.
Recidivism Prediction
8.4.1.2.
Bail Decision Support
8.4.1.3.
Sentencing Guidelines
8.4.2.
Evidence Analysis
8.4.2.1.
Forensic Data Analysis
8.4.2.2.
Pattern Recognition
8.4.2.3.
Chain of Custody
8.4.3.
Bias Detection and Mitigation
8.4.3.1.
Fairness Auditing
8.4.3.2.
Discrimination Prevention
8.4.3.3.
Equal Treatment Assurance
8.5.
Customer Experience and Marketing
8.5.1.
Recommendation Systems
8.5.1.1.
Product Recommendations
8.5.1.2.
Content Personalization
8.5.1.3.
Explanation Generation
8.5.2.
Customer Analytics
8.5.2.1.
Churn Prediction
8.5.2.2.
Lifetime Value Estimation
8.5.2.3.
Segmentation Analysis
8.5.3.
Marketing Optimization
8.5.3.1.
Campaign Effectiveness
8.5.3.2.
Target Audience Selection
8.5.3.3.
Attribution Analysis
8.6.
Cybersecurity
8.6.1.
Threat Detection
8.6.1.1.
Malware Classification
8.6.1.2.
Intrusion Detection
8.6.1.3.
Anomaly Analysis
8.6.2.
Security Operations
8.6.2.1.
Incident Response
8.6.2.2.
Forensic Analysis
8.6.2.3.
Risk Assessment
8.6.3.
Vulnerability Assessment
8.6.3.1.
System Weakness Identification
8.6.3.2.
Penetration Testing
8.6.3.3.
Security Auditing
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7. Evaluation of Explanations
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9. Challenges and Limitations