  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.
