Niki Kilbertus(University of Cambridge and Max Planck Institute for Intelligent Systems)
hosted by Bernt Schiele
"Fairness in machine learning"
Machine learning increasingly supports consequential decisions in health, lending, criminal justice or employment that affect the wellbeing of individual members or entire groups of our society. Such applications raise concerns about fairness, privacy violations and the long-term consequences of automated decisions in a social context. After a brief introduction to fairness in machine learning, I will highlight concrete settings with specific fairness or privacy ramifications and outline approaches to address them. I will conclude by embedding these examples into a broader context of socioalgorithmic systems and the complex interactions therein.
Bio: Niki Kilbertus is a final-year PhD student in the Cambridge-Tübingen program co-supervised by Bernhard Schölkopf and Carl Rasmussen. He is primarily interested in building socially beneficial, robust, and theoretically substantiated machine learning systems. Prior, Niki studied physics and mathematics at the University of Regensburg with research visits at Harvard and Stanford.
|Time:||Monday, 16.03.2020, 10:00|
|Place:||SB E 1 5 room 029 NOTE: Owing to the coronavirus situation, we ask if at all possible that interested parties attend the talk remotely (see instructions below). People who are unable to attend remotely may come listen to the talk in the Institute, but note that we will impose a hard limit of a maximum of 15 people physically present in the room.|
|Video:||videocast to KL room 111|