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Endless Summer School: AI Model Governance - Technical Research and the Legal Context for robust and secure AI Models
This two-part interdisciplinary series brings together legal and technical aspects of machine learning. The two sessions will kick-off with a legal perspective on current laws that deal with data privacy and machine learning IP, followed by talks from Vector researchers who will share their latest work on model governance. This seminal work addresses some of the legal questions that arise when operationalizing AI, such as: Can a machine unlearn? Can you share a model and protect your IP? How are optimization and training efficiency linked to adversarial robustness?

13 October 2021: Session 1
• A legal perspective on current data privacy laws
• Overview of trustworthy ML: Varun Chandrasekaran
• Machine unlearning: Nick Jia
• Model stealing: Mohammad Yaghini

3 November 2021: Session 2
• A legal perspective on ML and IP
• Confidential and Private Collaborative Learning - Adam Dziedzic
• Proof of Learning: Natalie Dullerud

For Vector Sponsors, health partners, vector researchers only
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