Measuring Inter-Bias Effects and Fairness-Accuracy Trade-Offs in GNN-Based Recommender Systems

Nikzad Chizari, Keywan Tajfar and María N. Moreno-García ()
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Nikzad Chizari: Department of Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain
Keywan Tajfar: Department of Statistics, University of Tehran, Tehran 1417614411, Iran
María N. Moreno-García: Department of Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain

Future Internet, 2025, vol. 17, issue 10, 1-16

Abstract: Bias in artificial intelligence is a critical issue because these technologies increasingly influence decision-making in a wide range of areas. The recommender system field is one of them, where biases can lead to unfair or skewed outcomes. The origin usually lies in data biases coming from historical inequalities or irregular sampling. Recommendation algorithms using such data contribute to a greater or lesser extent to amplify and perpetuate those imbalances. On the other hand, different types of biases can be found in the outputs of recommender systems, and they can be evaluated by a variety of metrics specific to each of them. However, biases should not be treated independently, as they are interrelated and can potentiate or mask each other. Properly assessing the biases is crucial for ensuring fair and equitable recommendations. This work focuses on analyzing the interrelationship between different types of biases and proposes metrics designed to jointly evaluate multiple interrelated biases, with particular emphasis on those biases that tend to mask or obscure discriminatory treatment against minority or protected demographic groups, evaluated in terms of disparities in recommendation quality outcomes. This approach enables a more comprehensive assessment of algorithmic performance in terms of both fairness and predictive accuracy. Special attention is given to Graph Neural Network-based recommender systems, due to their strong performance in this application domain.

Keywords: GNN-based recommender systems; Graph Neural Networks; unfairness; bias (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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