Bias amplification in experimental social networks is reduced by resampling
This addresses polarization in social media users by offering a practical mitigation strategy, though it is incremental as it builds on existing machine learning techniques.
The study tackled bias amplification in social networks by showing that information transmission through networks increased biased decision-making by 40% in controlled experiments, and proposed a resampling algorithm that reduced this amplification while preserving information sharing benefits.
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.