Academic Journal

Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions

التفاصيل البيبلوغرافية
العنوان: Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions
المؤلفون: Igoche, Bern Igoche, Matthew, Olumuyiwa, Bednar, Peter, Gegov, Alexander
المصدر: Journal of Computing Theories and Applications; Vol. 2 No. 1 (2024): JCTA 2(1) 2024; 65-85 ; 3024-9104
بيانات النشر: Universitas Dian Nuswantoro
سنة النشر: 2024
المجموعة: Ruang Publikasi Ilmiah Universitas Dian Nuswantoro
مصطلحات موضوعية: knowledge discovery in databases (KDD), structural causal model (SCM), ontology, local interpretable model-agnostic explanations (LIME), fairness
الوصف: This study employed knowledge discovery in databases (KDD) to extract and discover knowledge from the Benue State Polytechnic (Benpoly) admission database and used a structural causal model (SCM) ontological framework to represent the admission process in the Nigerian polytechnic education system. The SCM ontology identified important causal relations in features needed to model the admission process and was validated using the conditional independence test (CIT) criteria. The SCM ontology was further employed to identify and constrain input features causing bias in the local interpretable model-agnostic explanations (LIME) framework applied to machine learning (ML) black-box predictions. The ablation process produced more stable LIME explanations devoid of fairness bias compared to LIME without ablation, with higher prediction accuracy (91% vs. 89%) and F1 scores (95% vs. 94%). The study also compared the performance of different ML models, including Gaussian Naïve Bayes, Decision Trees, and Logistic Regression, before and after ablation. The limitation is that the SCM ontology is qualitative and context-specific, so the fair-LIME framework can only be extrapolated to similar contexts. Future work could compare other explanation frameworks like Shapley on the same dataset. Overall, this study demonstrates a novel approach to enforcing fairness in ML explanations by integrating qualitative SCM ontologies with quantitative ML/LIME methods.
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: English
Relation: https://publikasi.dinus.ac.id/index.php/jcta/article/view/10501/4542; https://publikasi.dinus.ac.id/index.php/jcta/article/view/10501
DOI: 10.62411/jcta.10501
الاتاحة: https://publikasi.dinus.ac.id/index.php/jcta/article/view/10501
https://doi.org/10.62411/jcta.10501
Rights: Copyright (c) 2024 Bern Igoche Igoche ; https://creativecommons.org/licenses/by/4.0
رقم الانضمام: edsbas.F8116F7B
قاعدة البيانات: BASE