February 14, 2020

194 words 1 min read

Fair, privacy-preserving, and secure ML

Fair, privacy-preserving, and secure ML

With ML becoming more mainstream, the side effects of machine learning and AI on our lives become more visible. You have to take extra measures to make machine learning models fair and unbiased. And awareness for preserving the privacy in ML models is rapidly growing. Mikio Braun explores techniques and concepts around fairness, privacy, and security when it comes to machine learning models.

Talk Title Fair, privacy-preserving, and secure ML
Speakers Mikio Braun (Zalando)
Conference Strata Data Conference
Conf Tag Make Data Work
Location New York, New York
Date September 24-26, 2019
URL Talk Page
Slides Talk Slides
Video

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.

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