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

Igoche ; Bern Igoche ; Matthew ; Olumuyiwa ; Bednar ; Peter ; Gegov ; Alexander
Journal of Computing Theories and Applications 📅 2024 Vol 2, No 1 🔗 DOI: 10.62411/jcta.10501
Abstract
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.
Keywords
knowledge discovery in databases (KDD); structural causal model (SCM); ontology; local interpretable model-agnostic explanations (LIME); fairness
How to Cite

Igoche, et al. (2024). Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions. Journal of Computing Theories and Applications, 2(1). https://doi.org/10.62411/jcta.10501

Igoche, Bern Igoche; Matthew, Olumuyiwa; Bednar, Peter; Gegov, Alexander, "Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions," Journal of Computing Theories and Applications, vol. 2, no. 1, 2024.

Igoche, Bern Igoche; Matthew, Olumuyiwa; Bednar, Peter; Gegov, Alexander. "Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions." Journal of Computing Theories and Applications, vol. 2, no. 1, 2024.

Igoche, Bern Igoche; Matthew, Olumuyiwa; Bednar, Peter; Gegov, Alexander. "Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions." Journal of Computing Theories and Applications 2, no. 1 (2024).

Igoche, et al. (2024) 'Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions', Journal of Computing Theories and Applications, 2(1). doi: 10.62411/jcta.10501.

Igoche, Bern Igoche; Matthew, Olumuyiwa; Bednar, Peter; Gegov, Alexander. Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions. Journal of Computing Theories and Applications. 2024;2(1).

Artikel Terkait

A Composite Centrality Framework for Evacuation Planning in Meso-Scale Spatial Networks with Semi-Structured Connectivity

Santoso, Jaya; Muliyana, Ana; Saragih, Asido; Pakpahan, Ridho; Chrisinta, Debora

A Systematic Review of Agentic AI in Healthcare: An Evidence-Informed Seven-Principle Framework

Prakash, Chandra; Sisodia, Avneesh ; Lind, Mary

Sentence-Level Sentiment Analysis of Indonesian App Reviews Using IndoBERTweet

Aqiilah, Inge Najwa; Saptono, Ristu; Syaifuddin, Akhmad

A Systematic Literature Review of Robustness-Aware Batik Motif Classification: Acquisition Variability, Feature Representation, and Learning Models

Priyambodo, Aji; Isnanto, R. Rizal; Sanjaya, Ridwan

YOLOv9s with Region-Dispersion Channel Spatial Attention for Robust Chili Leaf Disease Detection

Hidayat, Miwan Kurniawan; Na'am, Jufriadif; Ernawan, Ferda

Understanding Statistical and Temporal Representations for Large-Scale IoT DDoS Detection Through Ablation-Driven Analysis

Wicaksono, Daniel Nomolas; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Harkespan, Imanuel; Mohamed, Mohamad Afendee; Sambas, Aceng
Tren Sitasi Jurnal