Quantifying biases and inequalities in science

Every day our life is made easier by efficient algorithms that search and rank scientific information. Yet, these algorithms have an issue: they are trained on citation data that is ingrained with human biases. Therefore the output is inherently biased too, creating inequalities, raising concerns of discrimination, and harming economic 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.