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Inequality and inequity in network-based ranking and recommendation algorithms.
Lisette Espín-Noboa1,2,3, Claudia Wagner1,4,5, Markus Strohmaier1,4,6
1Complexity Science Hub, Vienna, Austria.
Scientific Reports
|February 8, 2022
Summary
Algorithms like PageRank and Who-to-Follow can create inequality and inequity in social networks. Network structure and user behavior significantly influence these biases, impacting minority representation in rankings.
Area of Science:
- Network Science
- Algorithmic Bias Studies
- Social Network Analysis
Background:
- Algorithms offer efficiency and objectivity but can perpetuate or introduce biases.
- Ranking algorithms in directed social networks may lead to unequal outcomes.
Purpose of the Study:
- To analyze the extent to which PageRank and Who-to-Follow (WTF) algorithms create inequality and inequity in directed social networks.
- To investigate the influence of network structure on rank distributions and algorithmic bias.
Main Methods:
- Development of a directed network model with preferential attachment and homophily (DPAH).
- Analysis of rank distributions generated by PageRank and WTF algorithms within the DPAH model.
Main Results:
- Inequality and inequity in rankings are positively correlated.
- Inequality is driven by preferential attachment, homophily, node activity, and edge density.
- Inequity is influenced by homophily and minority group size.
- Algorithms' impact on minority representation varies: reducing, replicating, or amplifying based on majority homophily.
Conclusions:
- Algorithmic ranking and recommendation systems can hinder equality and equity.
- Minority groups can strategically improve visibility by adjusting network connections (e.g., increasing out-degree or homophily).
- Understanding social and algorithmic mechanisms is crucial for mitigating bias in network-based systems.
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