While algorithms promise many benefits including efficiency, objectivity, and accuracy, they may also introduce or amplify biases. In this talk, I show how biases in our social networks are fed into and amplified by ranking and recommender systems. Drawing from social theories and fairness literature, we argue that biases in social connections need to be taken into consideration when designing people recommender systems.

Bio: Fariba Karimi is a data scientist who develops mathematical and computational models to study inequalities in socio-technical networks and algorithms. She is currently full professor of Data Science at the faculty of Computer Science and Biomedical Engineers at the Graz University of Technology.

Fariba Karimi received her doctorate from the University of Umea in 2015. She then spent four years researching at the computational social science department at Leibniz Institute for the Social Sciences in Cologne, Germany. Since March 2021, she has been the group lead of the “Network Inequality” group at Complexity Science Hub Institute in Vienna. Before joining TU Graz, she also served as a tenure track professor at the Department of Computer Science at Vienna University of Technology. In 2023, she received the prestigious Young Scientist Award from the German Physical Society for her contribution in modeling minorities and inequalities in networks.