Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and to evaluate mitigation strategies. Here we show under controlled laboratory conditions that information transmission through social networks amplifies motivational biases on a simple perceptual decision-making task. Participants in a large behavioral experiment showed increased rates of biased decision-making when part of a social network relative to asocial participants, across 40 independently evolving populations. Drawing on techniques from machine learning and Bayesian statistics, we identify a simple adjustment to content-selection algorithms that is predicted to mitigate bias amplification. This algorithm generates a sample of perspectives from within an individual's network that is more representative of the population as a whole. In a second large experiment, this strategy reduced bias amplification while maintaining the benefits of information sharing.

翻译：大型社会网络被认为通过扩大人们的偏见而助长了两极分化。然而,这些技术的复杂性使得难以确定负责的机制和评估缓解战略。在这里,我们在受控制的实验室条件下显示,通过社会网络传播信息会扩大简单观念决策任务方面的动机偏差。大型行为实验的参与者在社会网络的一部分与社会参与者相比,在40个独立演变的人口中显示出偏向性决策率的上升。我们利用机器学习和巴耶斯统计的技术,确定了对内容选择算法的简单调整,预测这种算法将减少偏差的扩大。这种算法从个人网络中产生一些观点样本,更能代表整个人口。在第二次大型实验中,这一战略减少了偏见的扩大,同时保持了信息共享的好处。