Fair, privacy-preserving, and secure ML
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 | Making Data Work |
| Location | London, United Kingdom |
| Date | April 30-May 2, 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.
