Sep 23–26, 2019

Fair, privacy preserving, and secure ML

Mikio Braun (Zalando SE)
2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 12/14
Secondary topics:  Ethics, Privacy and Security, Retail and e-commerce

Who is this presentation for?

Data Scientists, Managers, Product Managers.

Level

Intermediate

Description

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 knowledge

General idea of how ML works and what typical ML driven products are.

What you'll learn

Learn about limitations of ML and AI methods. Better understanding of how ML methods make use of data. Overview of often underrepresented topics like privacy and security when it comes to dealing with data driven approaches.
Photo of Mikio Braun

Mikio Braun

Zalando SE

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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