Human–AI adaptive dynamics drives the emergence of information cocoons
Human–AI adaptive dynamics drives the emergence of information cocoons
复制标题
人类-人工智能自适应动态推动信息茧的出现
DOI：
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发表时间：
2023
影响因子：
23.8
通讯作者：
Yong Li
中科院分区：
文献类型：
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作者：
J. Piao;Jiazhen Liu;Fang Zhang;Jun Su;Yong Li
尽管人工智能驱动的推荐算法被广泛采用来应对信息过载，但大量证据表明，它们正在构建同质化内容和观点的茧，进一步加剧了社会两极分化和偏见。遏制这些危险需要深入了解信息茧的起源。本文利用两个大数据集对现实世界中的信息茧进行了研究，发现大量用户被困在信息茧中。进一步的实证分析表明，人类与人工智能交互系统中的两种基本机制与信息多样性的丧失相关。在实证研究的基础上，我们推导了由这些基本机制控制的复杂人机交互系统中自适应信息动力学的机制模型。它允许我们预测三种状态之间的关键转变：多样化、部分信息茧和深度信息茧。我们的工作不仅从经验上追溯了现实世界中两种典型情景下的信息茧，而且从理论上揭示了信息茧产生的基本机制。我们为理解复杂人机交互系统中自适应信息动态导致的主要社会问题提供了一种理论方法。众所周知，社交媒体和新闻网站上基于人工智能的推荐系统可以将人类与各种信息隔离开来，最终将人类困在所谓的信息茧中，在那里他们暴露在狭窄的观点范围内。Li等人引入了一种自适应信息动力学模型，以揭示复杂的人机交互系统中信息茧的起源，并在两个大型现实世界数据集上测试了他们的发现。
Despite AI-driven recommendation algorithms being widely adopted to counter information overload, substantial evidence suggests that they are building cocoons of homogeneous contents and viewpoints, further aggravating social polarization and prejudice. Curbing these perils requires a deep insight into the origin of information cocoons. Here we investigate information cocoons in the real world using two large datasets and find that a large number of users are trapped in information cocoons. Further empirical analysis suggests that two ingredients, each corresponding to a fundamental mechanism in human–AI interaction systems, are correlated with the loss of information diversity. Grounded on the empirical findings, we derive a mechanistic model for the adaptive information dynamics in complex human–AI interaction systems governed by these fundamental mechanisms. It allows us to predict critical transitions between three states: diversification, partial information cocoons, and deep information cocoons. Our work not only empirically traces real-world information cocoons in two representative scenarios, but also theoretically unearths basic mechanisms governing the emergence of information cocoons. We provide a theoretical method for understanding major social issues resulting from adaptive information dynamics in complex human–AI interaction systems. It is widely known that AI-based recommendation systems on social media and news websites can isolate humans from diverse information, eventually trapping them in so-called information cocoons, where they are exposed to a narrow range of viewpoints. Li et al. introduce an adaptive information dynamics model to uncover the origin of information cocoons in complex human–AI interaction systems, and test their findings on two large real-world datasets.
影响因子：
29.9
作者：
Flamino, James;Galeazzi, Alessandro;Feldman, Stuart;Macy, Michael W.;Cross, Brendan;Zhou, Zhenkun;Serafino, Matteo;Bovet, Alexandre;Makse, Hernan A.;Szymanski, Boleslaw K.
通讯作者：
Szymanski, Boleslaw K.