Fair, privacy-preserving, and secure ML
Who is this presentation for?
- Data scientists, managers, and product managers
As machine learning becomes mainstream, the side effects of using machine learning and AI on our lives have become increasingly visible. However, awareness for preserving privacy in ML models is rapidly growing. Companies have learned, often through painful experience, that you have to take extra measures to make machine learning models fair and unbiased. For example, we now know it’s possible that private data within training examples can be retrieved from a learned model without extra measures.
Mikio Braun explores techniques and concepts around fairness, privacy, and security when it comes to machine learning models.
- A working knowledge of how ML works and what typical ML-driven products are
What you'll learn
- Learn limitations of ML and AI methods
- Gain a better understanding of how ML methods make use of data
- Get an overview of often-underrepresented topics like privacy and security when it comes to dealing with data-driven approaches
Mikio Braun is a principal engineer for search at Zalando, one of Europe’s biggest fashion platforms. He worked in research for a number of years before becoming interested in putting research results to good use in the industry. Mikio holds a PhD in machine learning.
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