Socially Beneficial Machine Learning
Niki Kilbertus (Ph.D. Student)
As machine learning touches upon all areas of our daily lives, it is increasingly deployed to make or support consequential decisions about individuals. Such applications raise concerns about privacy violations, the fairness of algorithms, as well as the long-term impact automated decisions might have on individuals and society as a whole. We address these concerns by building fair, privacy-preserving machine learning models and analyze their impact within the social context.
|Primary Host:||Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)|
|Exchange Host:||Carl Edward Rasmussen (University of Cambridge)|
|PhD Duration:||01 October 2016 - Ongoing|
|Exchange Duration:||01 September 2017 - 30 June 2018|