Veridical Data Science Towards Trustworthy AI

Overview

Syllabus

• Introduction to Veridical Data Science
Definition and Principles of Veridical Data Science
Importance in AI and Machine Learning
Overview of Statistical Thinking in Veridical Data Science
• Foundations of Trustworthy AI
Characteristics of Trustworthy AI Systems
Challenges in Building Trustworthy AI
Real-world Applications and Case Studies
• Data Quality and Reliability
Data Collection and Preprocessing
Assessing Data Quality: Metrics and Methods
Techniques for Ensuring Data Reliability
• Model Transparency and Explainability
Importance of Model Interpretability
Techniques for Interpretability: LIME, SHAP, etc.
Balancing Complexity and Transparency
• Fairness and Bias in AI
Identifying Bias in AI Models
Strategies for Mitigating Bias
Ethical Considerations in AI Development
• Robustness and Robust Statistics
Definition and Importance of Robustness in AI
Techniques for Building Robust Models
Evaluating Robustness: Stress Testing and Adversarial Examples
• Reproducibility in Data Science
Importance of Reproducibility in Research
Best Practices for Reproducible Workflows
Tools and Frameworks for Reproducibility
• AI Safety and Security
Identifying Security Risks in AI Systems
Techniques for Building Secure AI Models
Response Strategies for AI Failures
• Evaluation and Validation of AI Systems
Frameworks for Evaluating AI Performance
Validation Techniques: Cross-Validation, A/B Testing
Continuous Monitoring and Feedback Systems
• Future Directions for Veridical Data Science
Innovations in Trustworthy AI Techniques
The Role of Policy and Regulation
Envisioning the Future of Veridical Data Science and AI
• Course Wrap-up and Final Project
Reviewing Key Concepts and Ideas
Final Project: Designing a Trustworthy AI System
Discussion of Real-World Implementations and Career Applications

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Subjects

Computer Science