Affiliation:
1. Syracuse University, Syracuse, NY
2. Kumo.AI, Mountain View, California
Abstract
Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the
Information Unfairness (IUF)
metric, which quantifies inequality in access to information across protected groups. We then introduce
MinIUF
, an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved.
Publisher
Association for Computing Machinery (ACM)
Cited by
3 articles.
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1. Efficient Maximum Relative Fair Clique Computation in Attributed Graphs;Lecture Notes in Computer Science;2023-11-07
2. Data Context-Aware Web Information Retrieval;2023 XLIX Latin American Computer Conference (CLEI);2023-10-16
3. Reducing Access Disparities in Networks using Edge Augmentation✱;2023 ACM Conference on Fairness, Accountability, and Transparency;2023-06-12