{"type":"ScholarlyArticle","title":"AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society","authors":[{"name":"Yuxuan Hu","slug":"yuxuan-hu","given":"Yuxuan","family":"Hu","affiliation":"School of Information and Communication Technology , University of Tasmania"},{"name":"Shiqing Wu","slug":"shiqing-wu","given":"Shiqing","family":"Wu","affiliation":"School of Information and Communication Technology , University of Tasmania"},{"name":"Chenting Jiang","slug":"chenting-jiang","given":"Chenting","family":"Jiang","affiliation":"School of Information and Communication Technology , University of Tasmania; Tasmanian Institute of Agriculture , University of Tasmania"},{"name":"Weihua Li","slug":"weihua-li","given":"Weihua","family":"Li","affiliation":"School of Engineering , Computer and Mathematical Sciences , Auckland University of Technology"},{"name":"Quan Bai","slug":"quan-bai","given":"Quan","family":"Bai","affiliation":"School of Information and Communication Technology , University of Tasmania"},{"name":"Erin Roehrer","slug":"erin-roehrer","given":"Erin","family":"Roehrer","affiliation":"School of Information and Communication Technology , University of Tasmania"}],"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.","doi":"10.24963/ijcai.2022/705","url":"https://ijcai.org/proceedings/2022/705","pdf":"https://ijcai.org/proceedings/2022/0705.pdf","markdown":"https://ijcai.org/proceedings/2022/705.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/705","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":705,"pages":{"first":5080,"last":5086},"keywords":["Humans and AI: General","Humans and AI: Personalization and User Modeling","Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence","AI Ethics, Trust, Fairness: Societal Impact of AI"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987139","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985282","length_sec":568,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Adomavicius and Tuzhilin, 2005] G. 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