کتاب توسعه سیستم‌های هوش مصنوعی اخلاقی با فرمت AZW3 برای کتابخوان کیندل و فرمت EPUB

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره توسعه سیستم‌های هوش مصنوعی اخلاقی

موضوع کلی: هوش مصنوعی و مدل‌های زبانی بزرگ

موضوع میانی: هوش مصنوعی قابل اعتماد (Trustworthy AI)

📋 سرفصل‌های دوره

• 1. Foundations of Ethical AI in Large Models
• 2. Core Principles of Trustworthy AI
• 3. Ethical Impact Assessment Methodologies
• 4. Stakeholder Mapping and Analysis for AI Projects
• 5. Regulatory Frameworks and Global Compliance
• 6. Defining Ethical Objectives for AI Systems
• 7. Values Alignment and Goal Specification
• 8. Cross-Cultural Ethical Considerations
• 9. Ethical Data Collection Protocols
• 10. Data Annotation Ethics and Quality Assurance
• 11. Copyright and Data Sourcing Legalities
• 12. Privacy by Design in AI Development
• 13. Differential Privacy Implementation
• 14. Anonymization and Pseudonymization Techniques
• 15. Data Minimization and Necessity Principles
• 16. Identifying and Categorizing Dataset Bias
• 17. Quantitative Bias Measurement Metrics
• 18. Tracing Historical and Systemic Bias Sources
• 19. Fairness Definitions: Group vs. Individual
• 20. Fairness-Accuracy Trade-off Analysis
• 21. Evaluating Fairness with Appropriate Metrics
• 22. Data Preprocessing Bias Mitigation
• 23. Algorithmic Fairness Adjustments
• 24. Post-Processing Fairness Techniques
• 25. Interpretability versus Performance Balance
• 26. Fundamentals of Explainable AI (XAI)
• 27. Local Explanation Methods (e.g., LIME, SHAP)
• 28. Global Explanation and Feature Impact
• 29. Visualizing and Interpreting Model Decisions
• 30. Communicating Explainability to Non-Technical Stakeholders
• 31. Accountability Structures in AI Projects
• 32. Responsibility Assignment and RACI Matrices
• 33. Audit Trails and System Auditability
• 34. Ethical Logging and Metric Monitoring
• 35. Whistleblowing Channels and Reporting Mechanisms
• 36. AI Governance Models and Organizational Structures
• 37. Generating Ethical Impact Reports
• 38. Stakeholder Engagement and Consultation Processes
• 39. Human-in-the-Loop Design Principles
• 40. Managing Human Overreliance on AI
• 41. Human-AI Collaboration Patterns and Best Practices
• 42. User-Centric Ethical Interface Design
• 43. Building Trust through Usability and Transparency
• 44. Designing Ethical User Interfaces (UIs)
• 45. Long-Term Societal Impact Assessment
• 46. Sustainable AI Development Practices
• 47. Measuring and Reducing AI's Environmental Footprint
• 48. Energy-Efficient Model Training Strategies
• 49. Carbon-Aware AI Scheduling and Operations
• 50. Comprehensive Risk Assessment Methodologies
• 51. Robustness Against Adversarial Attacks
• 52. Ensuring Reliability and Validity in Deployments
• 53. Stress Testing and Resilience Evaluation
• 54. Ethical Sandboxing and Safe Testing Environments
• 55. Version Control for Ethical Parameters and Models
• 56. Continuous Ethical Evaluation Pipelines
• 57. Lifecycle-Wide Ethical Review Checkpoints
• 58. Emerging Ethical Challenges in Generative AI
• 59. Ethics of AI-Generated Content and Misinformation
• 60. Ethical Considerations in Autonomous Systems
• 61. Integrating Ethics into Continuous Integration/Deployment (CI/CD)
• 62. Ethical Code Review Practices for AI
• 63. Metrics and KPIs for Ethical AI Evaluation
• 64. Capstone: Designing a Complete Ethical AI System
• 65. Reflection and Future Directions in Ethical AI