Fairness in Opinion Dynamics

2026-01-07

arXiv.org

AI Summary

This study addresses the systematic bias in mainstream opinion dynamics models that disproportionately undermines prediction accuracy for marginalized groups. For the first time, it systematically identifies four distinct algorithmic bias patterns and proposes a context-aware fairness strategy that integrates demographic attributes with network topology. Leveraging the NetSense dataset and the CoDiNG opinion prediction model, the authors construct and evaluate three classifier types—demographic, topological, and hybrid. Experimental results demonstrate that approaches relying on a single feature modality are insufficient to mitigate bias effectively, whereas a multidimensional fusion framework significantly enhances predictive fairness and inclusive decision-making for underrepresented populations.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Ways in which people's opinions change are, without a doubt, subject to a rich tapestry of differing influences. Factors that affect how one arrives at an opinion reflect how they have been shaped by their environment throughout their lives, education, material status, what belief systems are they subscribed to, and what socio-economic minorities are they a part of. This already complex system is further expanded by the ever-changing nature of one's social network. It is therefore no surprise that many models have a tendency to perform best for the majority of the population and discriminating those people who are members of various marginalized groups . This bias and the study of how to counter it are subject to a rapidly developing field of Fairness in Social Network Analysis (SNA). The focus of this work is to look into how a state-of-the-art model discriminates certain minority groups and whether it is possible to reliably predict for whom it will perform worse. Moreover, is such prediction possible based solely on one's demographic or topological features? To this end, the NetSense dataset, together with a state-of-the-art CoDiNG model for opinion prediction have been employed. Our work explores how three classifier models (Demography-Based, Topology-Based, and Hybrid) perform when assessing for whom this algorithm will provide inaccurate predictions. Finally, through a comprehensive analysis of these experimental results, we identify four key patterns of algorithmic bias. Our findings suggest that no single paradigm provides the best results and that there is a real need for context-aware strategies in fairness-oriented social network analysis. We conclude that a multi-faceted approach, incorporating both individual attributes and network structures, is essential for reducing algorithmic bias and promoting inclusive decision-making.

Problem

• Fairness
• Opinion Dynamics
• Algorithmic Bias
• Social Network Analysis
• Minority Groups

Innovation

• algorithmic bias
• fairness in SNA
• opinion dynamics
• hybrid fairness model
• minority group prediction