AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society
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Hu, Yuxuan
Wu, Shiqing
Jiang, Chenting
Li, Weihua
Bai, Quan
Roehrer, Erin
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International Joint Conferences on Artificial Intelligence Organization
Abstract
AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.
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Keywords
46 Information and Computing Sciences, 4608 Human-Centred Computing, Humans and AI, Personalization and User Modeling, AI Ethics, Trust, Fairness, Societal Impact of AI, Agent-based and Multi-agent Systems, Agent-Based Simulation and Emergence
Source
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: AI for Good. {IJCAI-22}. Vienna, 2022. Pages 5080-5086.
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This is the Author's Accepted Manuscript of a paper published in the proceedings of the 31st Copyright International Joint Conference on Artificial Intelligence © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved. The final, published version of the paper is available, free access, at (see Publisher's version).