Machine Learning algorithms shape the lives of many individuals by deciding who receives a loan, gets hired for a job, what kind of promotional offers people see on their social media feeds and even what medical care is optimal for them.
All these decisions have in common that they raise the question on how a good should be distributed: this is the classical question of fairness discussed in the realms of philosophy. Research on “Fairness in Machine Learning” tries to bridge the gap between our fuzzy notions of fairness and the mathematical exactness needed to incorporate these principles into computers.
The “Fairness of algorithms” group reviews this research and discusses its promises and shortcomings. We share the knowledge gained from our analysis by writing blogposts that are accessible to non-experts and develop workshops that introduce Data Scientists to these concepts by using hands-on examples. Our goal is to raise awareness about fairness issues and enable developers and the public to understand and face the challenges introduced by algorithmic decision making.
Speaker: Gabriel Lindner
Members: Gunnar König, Max Speck, Teodora Pandeva, Leni Rohleder, Chiara Ullstein, Julia Pfeiffer, Julius Morandell