Shift your Thinking: A Practical View of Privacy in AI/ML
Hosted by Katharine Jarmul
In this video
What you'll learn
Identifying common privacy mistakes
Classic approaches to privacy or simple fixes don't address the way we build AI systems
Assessing real risks
Rather than looking at all risk in all ways, focus on what risks affect your architecture, system and model choices.
Focusing on what's possible
Adjust your efforts to address where you can make impact. Where can you engineer privacy into the workflow?
Why this topic matters
Too often privacy efforts fail because they focus on the wrong parts of the AI/ML lifecycle.
In this lighting lesson, you'll cut through risks that you can't address and focus on measurable impact for real-world AI/ML workflows.
By making privacy engineering actionable, you'll walk away with a clearer roadmap to addressing privacy issues in your setup and building safer system
You'll learn from
Katharine Jarmul
Privacy in Machine Learning Systems, Author of Practical Data Privacy
Katharine Jarmul focuses her work and research on privacy and security in data science, deep learning and AI. She is author of the well received O'Reilly book Practical Data Privacy (O'Reilly 2023) and has more than 10 years experience in machine learning/AI where she has helped build large scale AI systems with privacy and security built in. You can follow her work via her newsletter, Probably Private or on her website.
Go deeper with a course
Practical AI Privacy
Katharine Jarmul
Author of Practical Data Privacy, Specialist in AI/ML Systems
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