Trust and Distrust in ML: Privacy, Verification and Robustness

Overview

Immerse yourself in a thought-provoking exploration of trust and distrust in machine learning with the esteemed Shafi Goldwasser. This Emmy Noether Lecture addresses the pressing issues of privacy, the critical methods for verification, and the robust challenges present in today's AI landscape.

Stream the lecture now on YouTube and deepen your understanding of these pivotal topics within Artificial Intelligence and Computer Science.

Syllabus

• Introduction to Trust in Machine Learning
Definition and Importance of Trust in ML
Overview of Privacy, Verification, and Robustness
• Privacy Concerns in Machine Learning
Understanding Privacy Implications
Differential Privacy
Federated Learning and Privacy
Techniques for Anonymization and Data Sanitization
• Verification Methods in Machine Learning
Overview of Verification in ML Systems
Formal Verification Techniques
Runtime Verification and Monitoring
Tools and Frameworks for ML Model Verification
• Robustness Challenges in Machine Learning
Defining Robustness in ML Systems
Adversarial Attacks and Defense Mechanisms
Generalization and Overfitting
Robustness in Model Deployment and Maintenance
• Case Studies and Real-world Applications
Analysis of Trust in ML Applications
Lessons Learned from Industry and Research
• Ethical and Societal Implications
Bias and Fairness in Machine Learning
Ethical Considerations in ML Deployment
Future Directions and Open Research Questions
• Conclusion and Recap
Integrating Privacy, Verification, and Robustness in ML
Key Takeaways and Best Practices
• Guest Lecture with Shafi Goldwasser
Insights from Research and Applications
Q&A Session with Participants
• Final Evaluation and Project
Practical Application of Course Concepts
Student Presentations and Peer Feedback

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Subjects

Computer Science