ON DYADIC FAIRNESS: EXPLORING AND MITIGATING BIAS IN GRAPH CONNECTIONS

Abstract

Disparate impact has raised serious concerns in machine learning applications and its societal impacts. In response to the need of mitigating discrimination, fairness has been regarded as a crucial property in algorithmic designs. In this work, we study the problem of disparate impact on graph-structured data. Specifically, we focus on dyadic fairness, which articulates a fairness concept that a predictive relationship between two instances should be independent of the sensitive attributes. Based on this, we theoretically relate the graph connections to dyadic fairness on link predictive scores in learning graph neural networks, and reveal that regulating weights on existing edges in a graph contributes to dyadic fairness conditionally. Subsequently, we propose our algorithm, FairAdj, to empirically learn a fair adjacency matrix with proper graph structural constraints for fair link prediction, and in the meanwhile preserve predictive accuracy as much as possible. Empirical validation demonstrates that our method delivers effective dyadic fairness in terms of various statistics, and at the same time enjoys a favorable fairness-utility tradeoff.

1. INTRODUCTION

The scale of graph-structured data has grown explosively across disciplines (e.g., social networks, telecommunication networks, and citation networks), calling for robust computational techniques to model, discover, and extract complex structural patterns hidden in big graph data. Research work has been proposed for inference learning on potential connections (Liben-Nowell & Kleinberg, 2007) , and corresponding algorithms can be used for high-quality link prediction and recommendations (Adamic & Adar, 2003; Sarwar et al., 2001; Qi et al., 2006) . In this work, we study the potential disparate impact in the prediction of dyadic relationships between two instances within a homogeneous graph. Despite the wide applications of link prediction algorithms, serious concerns raised by disparate impact (Angwin et al., 2016; Barocas & Selbst, 2016; Bose & Hamilton, 2019a; Liao et al., 2020) should also be reckoned with by algorithm designers. In an algorithmic context, disparate impact often describes the disparity in influential decisions which essentially derives from the characteristics protected by anti-discrimination laws or social norms. Unfortunately, this negative impact derived from biased data and conventional algorithms occurs in many applications including link prediction. One example is that a user recommender system follows the proximity principle (individuals are more likely to interact with similar individuals) or existing connections with intrinsic bias. Such an operating mode would deliver biased recommendations dominated by sensitive attributes. For example, users with the same religion or ethnic group are more likely to be recommended to a user, and consequently generate segregation in social relations by long-term accumulation (Hofstra et al., 2017) . Another example can be noticed in news streaming. When a news app has collected the political profile from a user, in pursuit of the user preference in news streaming, the system might only deliver politicking that the user is predisposed to agree with, therefore skews a user's scope and narrows the view by selectively displaying reality (Pariser, 2011) . To alleviate these concerns, an algorithm should perform a link prediction without being biased by the sensitive attribute of the two instances, and should also stream diverse and preferred recommendations. Motivated by the potential bias in real cases, in this paper we propose dyadic fairness for the link prediction problem in homogeneous graphs, where the dyadic fairness criterion expects the predictions

