Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

Farzan Masrour · Tyler Wilson · Heng Yan · Pang‐Ning Tan · Abdol H. Esfahanian

82 citations0/0 files storedProceedings of the AAAI Conference on Artificial Intelligence

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Ethics and Social Impacts of AI Complex Network Analysis Techniques Advanced Graph Neural Networks

Abstract

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.

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References

2001Birds of a Feather: Homophily in Social Networks 2012Fairness through awareness 2017Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2016Equality of Opportunity in Supervised Learning 2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2015Certifying and Removing Disparate Impact 2013Learning Fair Representations 2017Counterfactual Fairness

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