Quantifying biases and inequalities in science

Every day our life is made easier by efficient measures and algorithms that quantify, search, and rank scientific information. Yet, these measures and algorithms have an issue: they are trained on citations, which are ingrained with human biases. Therefore the output is inherently biased too, creating inequalities, raising concerns of discrimination, even harming growth. In this talk, I focus on recent quantitative efforts in science studies to (1) uncover bias mechanisms in science, (2) use this knowledge about biases to uncover inequalities in the scientific enterprise, and (3) create fair metrics and algorithms.

Roberta Sinatra is Professor in Computational Social Science at the University of Copenhagen (KU), and holds visiting positions at IT University of Copenhagen (ITU), ISI Foundation (Turin, Italy) and Complexity Science Hub (Vienna, Austria). She co-founded the NEtwoRks, Data, and Society (NERDS) Research group at ITU, which she led in 2019-2022, and is a co-lead of the pioneer centre for AI in Copenhagen. Her research is at the forefront of network science, data science, and computational social science. Roberta did her BSc, MSc and PhD in Physics at the University of Catania, Italy. Her research has been published in top-tier venues like Nature and Science, and has been featured in The New York Times, Forbes, The Economist, The Guardian, The Washington Post, among other major media outlets. Her research has been awarded the Complex Systems Society Junior prize, the DPG Young Scientist Award for Socio- and Econophysics, and a Villum Young Investigator grant.