Bias Reduction in Social Networks through Agent-Based Simulations

TL;DR

This paper presents an agent-based simulation model to evaluate how different recommender system algorithms influence perception biases in social networks, highlighting the role of network structure in bias mitigation.

Contribution

It introduces a simple agent-based model to compare recommender systems and demonstrates that network-aware greedy algorithms can reduce perception biases effectively.

Findings

Network structure significantly impacts bias in recommendations.

A greedy algorithm based on network properties reduces perception biases.

Simulation results show comparable bias reduction to random feeds.

Abstract

Online social networks use recommender systems to suggest relevant information to their users in the form of personalized timelines. Studying how these systems expose people to information at scale is difficult to do as one cannot assume each user is subject to the same timeline condition and building appropriate evaluation infrastructure is costly. We show that a simple agent-based model where users have fixed preferences affords us the ability to compare different recommender systems (and thus different personalized timelines) in their ability to skew users' perception of their network. Importantly, we show that a simple greedy algorithm that constructs a feed based on network properties reduces such perception biases comparable to a random feed. This underscores the influence network structure has in determining the effectiveness of recommender systems in the social network context and offers a tool for mitigating perception biases through algorithmic feed construction.