The Collective Intelligence Lab, New Jersey Institute of Technology, Newark, NJ, United States of America.
School of Business, Economics and Law at the University of Gothenburg, Gothenburg, Sweden.
PLoS One. 2022 Aug 2;17(8):e0272168. doi: 10.1371/journal.pone.0272168. eCollection 2022.
Algorithmic agents, popularly known as bots, have been accused of spreading misinformation online and supporting fringe views. Collectives are vulnerable to hidden-profile environments, where task-relevant information is unevenly distributed across individuals. To do well in this task, information aggregation must equally weigh minority and majority views against simple but inefficient majority-based decisions. In an experimental design, human volunteers working in teams of 10 were asked to solve a hidden-profile prediction task. We trained a variational auto-encoder (VAE) to learn people's hidden information distribution by observing how people's judgments correlated over time. A bot was designed to sample responses from the VAE latent embedding to selectively support opinions proportionally to their under-representation in the team. We show that the presence of a single bot (representing 10% of team members) can significantly increase the polarization between minority and majority opinions by making minority opinions less prone to social influence. Although the effects on hybrid team performance were small, the bot presence significantly influenced opinion dynamics and individual accuracy. These findings show that self-supervized machine learning techniques can be used to design algorithms that can sway opinion dynamics and group outcomes.
算法代理，通常称为机器人，被指控在网上传播错误信息并支持边缘观点。集体很容易受到隐藏配置文件环境的影响，在这种环境中，与任务相关的信息在个体之间分布不均。为了在这项任务中表现出色，信息聚合必须平等权衡少数派和多数派观点，反对简单但效率低下的基于多数派的决策。在一个实验设计中，我们要求 10 人一组的人类志愿者解决一个隐藏配置文件预测任务。我们通过观察人们的判断随时间如何相关，训练了一个变分自动编码器（VAE）来学习人们的隐藏信息分布。设计了一个机器人从 VAE 潜在嵌入中采样响应，以根据团队中的代表性不足成比例地选择性支持意见。我们表明，单个机器人（代表团队成员的 10%）的存在可以通过使少数派意见不太容易受到社会影响，从而显著增加少数派和多数派意见之间的极化。尽管对混合团队绩效的影响很小，但机器人的存在显著影响了意见动态和个人准确性。这些发现表明，自我监督的机器学习技术可用于设计算法，以影响意见动态和群体结果。
