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http://dspace.lib.uom.gr/handle/2159/30313
Author: | Μούσια, Αθηνά |
Title: | Fairness in predictive analytics: integrating bias detection, mitigation, and explainability in machine learning models |
Date Issued: | 2024 |
Department: | Πρόγραμμα Μεταπτυχιακών Σπουδών Αναλυτική των Επιχειρήσεων και Επιστήμη των Δεδομένων |
Supervisor: | Ταραμπάνης, Κωνσταντίνος |
Abstract: | This thesis presents a critical analysis of machine learning algorithms within the realm of educational predictive analytics, with a particular emphasis on detecting and mitigating socio-economic biases. The research employs an analytical framework comprising bias detection techniques to identify inherent biases in algorithms or datasets, bias mitigation models to adjust these elements and reduce socio-economic disparities, and explainability methods to elucidate the decision-making mechanisms of the algorithms. |
Keywords: | Ethical AI Machine Learning |
Information: | Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024. |
Rights: | Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές |
Appears in Collections: | ΠΜΣ Αναλυτική των Επιχειρήσεων και Επιστήμη των Δεδομένων (Μ) |
Files in This Item:
File | Description | Size | Format | |
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MousiaAthinaMsc2024.pdf | 2.7 MB | Adobe PDF | View/Open |
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