Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or margin...
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MDPI AG
2023-02-01
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| Online Access: | https://www.mdpi.com/2078-2489/14/2/131 |
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| author | Nikzad Chizari Keywan Tajfar María N. Moreno-García |
| author_facet | Nikzad Chizari Keywan Tajfar María N. Moreno-García |
| author_sort | Nikzad Chizari |
| collection | DOAJ |
| description | In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed. |
| first_indexed | 2024-03-11T08:40:06Z |
| format | Article |
| id | doaj.art-c56beed2d0824fc6808179794207f03c |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T08:40:06Z |
| publishDate | 2023-02-01 |
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| spelling | doaj.art-c56beed2d0824fc6808179794207f03c2023-11-16T21:12:44ZengMDPI AGInformation2078-24892023-02-0114213110.3390/info14020131Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender SystemsNikzad Chizari0Keywan Tajfar1María N. Moreno-García2Department of Computer Science and Automation, Science Faculty, University of Salamanca, Plaza de los Caídos sn, 37008 Salamanca, SpainCollege of Science, School of Mathematics, Statistics, and Computer Science, Department of Statistics, University of Tehran, Tehran 1417935840, IranDepartment of Computer Science and Automation, Science Faculty, University of Salamanca, Plaza de los Caídos sn, 37008 Salamanca, SpainIn today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed.https://www.mdpi.com/2078-2489/14/2/131recommender systemsGNN (graph neural network)bias amplificationfairnesssensitive features |
| spellingShingle | Nikzad Chizari Keywan Tajfar María N. Moreno-García Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems Information recommender systems GNN (graph neural network) bias amplification fairness sensitive features |
| title | Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems |
| title_full | Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems |
| title_fullStr | Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems |
| title_full_unstemmed | Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems |
| title_short | Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems |
| title_sort | bias assessment approaches for addressing user centered fairness in gnn based recommender systems |
| topic | recommender systems GNN (graph neural network) bias amplification fairness sensitive features |
| url | https://www.mdpi.com/2078-2489/14/2/131 |
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