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Privacy Engineering: Safeguarding AI & ML Systems in a Data-Driven Era; With Guest Katharine Jarmul

The MLSecOps Podcast

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Privacy Risks in Machine Learning Models

This chapter discusses the data privacy and security risks associated with machine learning models, including challenges in handling personal data, overfitting outliers, and the limitations of current regulatory frameworks. It explores potential solutions such as data lineage tracking, consent questions, and model cards. The chapter also emphasizes the importance of population selection, better documentation, and privacy engineering to align technology with social boundaries.

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