AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society

AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society

Yuxuan Hu, Shiqing Wu, Chenting Jiang, Weihua Li, Quan Bai, Erin Roehrer

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
AI for Good. Pages 5080-5086. https://doi.org/10.24963/ijcai.2022/705

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.
Keywords:
Humans and AI: General
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