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FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.

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Title: FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.
Authors: Kaden, Marika1 (AUTHOR) kaden1@hs-mittweida.de, Schubert, Ronny1 (AUTHOR), Voigt, Julius1 (AUTHOR), Reuss, Lynn1 (AUTHOR), Engelsberger, Alexander1 (AUTHOR), Lövdal, Sofie2,3 (AUTHOR), van den Brandhof, Elina L.2,4 (AUTHOR), Biehl, Michael2,5 (AUTHOR), Villmann, Thomas1,6 (AUTHOR) villmann@hs-mittweida.de
Source: Neurocomputing. May2026, Vol. 678, pN.PAG-N.PAG. 1p.
Subjects: Algorithmic bias, Classification, Machine learning, Decision making, Discrimination (Sociology), Data analysis, Vector quantization
Abstract: Developing fair classification models is a crucial aspect of machine learning research. However, unintended distortion in training data - biased data - can lead to discriminatory decisions. In this paper, we developed a workflow for detecting and mitigating bias in data using a shallow, interpretable machine learning model: the Generalized Matrix Learning Vector Quantization. We extend the approach by a relevance-based analysis to identify and reduce bias in the data. Combining similarity metric adaptation and relevance-based analysis, we can develop fair classification models that minimize the influence of bias in the data. Our results demonstrate that this method is effective in reducing bias in classification models and therefore supports fair decision-making. • Model native derived solution for bias mitigation avoiding surrogate methods. • Workflow to examine a bias hypothesis and, if applicable, mitigate the bias in a transparent manner. • Mitigated bias model performance is evaluated for significant differences compared to the original models performance. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Developing fair classification models is a crucial aspect of machine learning research. However, unintended distortion in training data - biased data - can lead to discriminatory decisions. In this paper, we developed a workflow for detecting and mitigating bias in data using a shallow, interpretable machine learning model: the Generalized Matrix Learning Vector Quantization. We extend the approach by a relevance-based analysis to identify and reduce bias in the data. Combining similarity metric adaptation and relevance-based analysis, we can develop fair classification models that minimize the influence of bias in the data. Our results demonstrate that this method is effective in reducing bias in classification models and therefore supports fair decision-making. • Model native derived solution for bias mitigation avoiding surrogate methods. • Workflow to examine a bias hypothesis and, if applicable, mitigate the bias in a transparent manner. • Mitigated bias model performance is evaluated for significant differences compared to the original models performance. [ABSTRACT FROM AUTHOR]
ISSN:09252312
DOI:10.1016/j.neucom.2026.133200