Bias amplification in experimental social networks is reduced by resampling

TL;DR

This study demonstrates that social networks can amplify biases in decision-making, but a simple algorithmic adjustment can reduce this effect while preserving information sharing benefits.

Contribution

The paper introduces a novel content-selection algorithm that mitigates bias amplification in social networks based on Bayesian sampling techniques.

Findings

Bias amplification increases in social networks during decision tasks.

A Bayesian-inspired sampling algorithm reduces bias amplification.

The adjusted algorithm maintains information sharing benefits.

Abstract

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.