Inequality and Inequity in Network-based Ranking and Recommendation Algorithms

TL;DR

This paper analyzes how PageRank and Who-to-Follow algorithms create inequality and inequity in social network rankings, influenced by network structure and user behaviors, and explores strategies minorities can use to improve visibility.

Contribution

It introduces a directed network model with preferential attachment and homophily to study the impact of network structure on ranking inequality and inequity, revealing key influencing factors.

Findings

Inequality correlates positively with inequity.

Network structure influences rank distribution and minority visibility.

Minorities can improve their ranking by strategic connections.

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 minority size. In particular, these two algorithms reduce, replicate and amplify the representation of minorities in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when this representation is reduced, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their out-degree or homophily when majorities are also homophilic. These findings shed light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms.