Unequal Opportunities in Multi-hop Referral Programs

TL;DR

This paper investigates how multi-hop referral programs in social networks can unintentionally amplify structural biases against disadvantaged groups, depending on network structure and referral strategies, even without explicit discrimination.

Contribution

It provides a theoretical and empirical analysis of fairness in multi-hop referral programs, revealing conditions under which bias amplification occurs.

Findings

Certain referral strategies can increase bias in higher hops.

Bias amplification depends on network structure and referral constraints.

Unconstrained referral strategies can also lead to increased bias.

Abstract

As modern social networks allow for faster and broader interactions with friends and acquaintances, online referral programs that promote sales through existing users are becoming increasingly popular. Because it is all too common that online networks reproduce historical structural bias, members of disadvantaged groups often benefit less from such referral opportunities. For instance, one-hop referral programs that distribute rewards only among pairs of friends or followers may offer less rewards and opportunities to minorities in networks where it was proved that their degrees is statistically smaller. Here, we examine the fairness of general referral programs, increasingly popular forms of marketing in which an existing referrer is encouraged to initiate the recruitment of new referred users over multiple hops. While this clearly expands opportunities for rewards, it remains unclear whether it helps addressing fairness concerns, or make them worse. We show, from studying 4 real-world networks and performing theoretical analysis on networks created with minority-majority affiliations and homophily, that the change of bias in multi-hop referral programs highly depends on the network structures and the referral strategies. Specifically, under three different constrained referral strategies which limit the number of referrals each person can share to a fixed number, we show that even with no explicit intention to discriminate and without access to sensitive attributes such as gender and race, certain referral strategies can still amplify the structural biases further when higher hops are allowed. Moreover, when there is no constraint on the number of referrals each person can distribute and when the effect of referral strategies is removed, we prove a precise condition under which the bias in 1-hop referral programs is amplified in higher-hop referral programs.