Veridical Data Science Towards Trustworthy AI

Overview

Explore the principles of veridical data science and its application in developing trustworthy AI systems with Bin Yu from UC Berkeley.

Syllabus

• Introduction to Veridical Data Science
Definition and relevance
Historical context and evolution
Overview of the course structure
• Principles of Veridical Data Science
Clarity and transparency
Predictability and reliability
Reproducibility and replicability
• Data Collection and Preprocessing
Data quality and integrity
Handling bias in data
Data cleaning techniques
• Statistical Inference for Trustworthy AI
Concepts of statistical inference
Ensuring validity and reliability
Managing uncertainty
• Machine Learning for Trustworthy AI
Understanding model interpretability
Balancing complexity and simplicity
Avoiding overfitting and underfitting
• Model Evaluation and Validation
Performance metrics and evaluation
Cross-validation techniques
Bias-variance trade-off
• Ethical Considerations and Bias Mitigation
Identifying and mitigating algorithmic bias
Ensuring fairness and equity
Ethical frameworks in AI
• Case Studies and Real-World Applications
Analysis of successful deployment of trustworthy AI systems
Lessons learned from failures
Exemplars of veridical data science
• Tools and Technologies for Veridical Data Science
Software platforms and libraries
Data visualization techniques
Open-source tools and resources
• Future Trends in Trustworthy AI
Emerging challenges and opportunities
Innovations in veridical data science
Next steps for research and development
• Course Review and Capstone Project
Integration of learned concepts
Project design and implementation
Student presentations and feedback

Subjects

Computer Science