Inequality and Inequity in Network-based Ranking and Recommendation Algorithms

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 under which circumstances 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 mainly driven by homophily. In particular, these two algorithms amplify, replicate and reduce inequity in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when inequity is amplified, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their homophily when majorities are also homophilic. These findings shed light on social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms.