Fair, privacy preserving, and secure ML
Who is this presentation for?Data Scientists, Managers, Product Managers.
With ML becoming more and more mainstream, the side effects of using machine learning and AI on our lives become more and more visible. Companies have learned, often through painful experience, that one has to take extra measures to make machine learning models fair and unbiased so that they don’t pick up on hidden biases in the data. In addition, awareness for preserving the privacy in ML models is rapidly growing. For example, it is possible that private data within the training examples can be retrieved from a learned model without extra measures. In this talk, we will look at techniques and concepts around fairness, privacy, and security when it comes to machine learning models.
Prerequisite knowledgeGeneral idea of how ML works and what typical ML driven products are.
What you'll learn
Mikio Braun is 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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