Abstract: Algorithmic decision making is pervasive, the prices we pay, the news or movies we see, the jobs or credits we get are advised by algorithms. Not so long ago the public used to think that decision making by computers is inherently objective, but realization that models learned from data are not more objective than the data on which they have been trained is becoming common. Fairness-aware machine learning has emerged as a discipline about ten years ago with the main goal to correct algorithmically for potential biases towards sensitive groups of people. The talk will discuss the main challenges, existing solutions and current trends in this research area.

Speaker: Indre Zliobaite

Affiliation: University of Helsinki

Place of Seminar: University of Helsinki