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Evaluating Fairness Strategies in Educational Data Mining: A Comparative Study of Bias Mitigation Techniques

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Publicado en:Electronics vol. 14, no. 9 (2025), p. 1856
Autor principal: Raftopoulos, George
Otros Autores: Davrazos Gregory, Kotsiantis Sotiris
Publicado:
MDPI AG
Materias:
Comparative studies
Algorithms
Socioeconomic factors
Students
Preprocessing
Data mining
Bias
Datasets
Machine learning
Education
Acceso en línea:Citation/Abstract
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Resumen:Ensuring fairness in machine learning models applied to educational data is crucial for mitigating biases that can reinforce systemic inequities. This paper compares various fairness-enhancing algorithms across preprocessing, in-processing, and post-processing stages. Preprocessing methods such as Reweighting, Learning Fair Representations, and Disparate Impact Remover aim to adjust training data to reduce bias before model learning. In-processing techniques, including Adversarial Debiasing and Prejudice Remover, intervene during model training to directly minimize discrimination. Post-processing approaches, such as Equalized Odds Post-Processing, Calibrated Equalized Odds Post-Processing, and Reject Option Classification, adjust model predictions to improve fairness without altering the underlying model. We evaluate these methods on educational datasets, examining their effectiveness in reducing disparate impact while maintaining predictive performance. Our findings highlight tradeoffs between fairness and accuracy, as well as the suitability of different techniques for various educational applications.
ISSN:2079-9292
DOI:10.3390/electronics14091856
Fuente:Advanced Technologies & Aerospace Database