Modeling algorithmic bias: simplicial complexes and evolving network topologies

Applied Network Science - 2022
Valentina Pansanella1, Giulio Rossetti2, Letizia Milli2,3
1Faculty of Science, Scuola Normale Superiore, Pisa, Italy
2Institute of Information Science and Technologies “Alessandro Faedo” (ISTI), National Research Council (CNR), Pisa, Italy
3Department of Computer Science, University of Pisa, Pisa, Italy

Tóm tắt

Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content—co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-called Algorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erdős–Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating the Adaptive Algorithmic Bias model. The second one explicitly models a peer-pressure mechanism where a majority—if there is one—can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both “close-mindedness” and algorithmic filtering.

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Applied Network Science

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