Bias Explained: Pushing Algorithmic Fairness with Models and Experiments

Bias Explained: Pushing Algorithmic Fairness with Models and Experiments
Algorithms for ranking scientific information have an issue: they use citations, which are ingrained with human biases. Therefore, their output is also biased, creating inequalities and raising concerns of discrimination. This project aims to uncover the mathematical bias mechanisms that drive different citation trajectories given same quality, and to use them for creating fair algorithms. The grant will allow the recruitment of one PhD student and two postdocs.