Copertina

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

Salvato in:
Pubblicato in:Electronics vol. 14, no. 9 (2025), p. 1856
Autore principale: Raftopoulos, George
Altri autori: Davrazos Gregory, Kotsiantis Sotiris
Pubblicazione:
MDPI AG
Soggetti:
Comparative studies
Algorithms
Socioeconomic factors
Students
Preprocessing
Data mining
Bias
Datasets
Machine learning
Education
Accesso online:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract: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
Fonte:Advanced Technologies & Aerospace Database