Inequality and inequity in network-based ranking and recommendation algorithms.

ABSTRACT: Though algorithms promise many benefits including efficiency, objectivity and accuracy, they may also introduce or amplify biases. Here we study two well-known algorithms, namely PageRank and Who-to-Follow (WTF), and show to what extent their ranks produce inequality and inequity when applied to directed social networks. To this end, we propose a directed network model with preferential attachment and homophily (DPAH) and demonstrate the influence of network structure on the rank distributions of these algorithms. Our main findings suggest that (i) inequality is positively correlated with inequity, (ii) inequality is driven by the interplay between preferential attachment, homophily, node activity and edge density, and (iii) inequity is driven by the interplay between homophily and minorit

SUBMITTER: Espin-Noboa L 

PROVIDER: S-EPMC8821643 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature