most citedAn Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.HC2026

ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning

Thang Doan Viet, Anh Nguyen Hoang, Tinh Luong Son +3

This paper presents ConnectED, a human-centered AI system that supports the full instructional lifecycle in Vietnamese education by linking curriculum-aligned lesson design, intera…

cs.LG2026

FairFinGAN: Fairness-aware Synthetic Financial Data Generation

Tai Le Quy, Dung Nguyen Tuan, Trung Nguyen Thanh +3

Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems. In this work, we propose FairFinGAN, a WGAN-based framework designed to gene…

cs.LG20261 cited

An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems

Huyen Giang Thi Thu, Thang Viet Doan, Ha-Bang Ban +1

The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning techniq…

cs.LG2025

A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective

Siamak Ghodsi, Amjad Seyedi, Tai Le Quy +2

Fair graph clustering seeks partitions that respect network structure while maintaining proportional representation across sensitive groups, with applications spanning community de…

cs.LG2025

Constraint-Reduced MILP with Local Outlier Factor Modeling for Plausible Counterfactual Explanations in Credit Approval

Trung Nguyen Thanh, Huyen Giang Thi Thu, Tai Le Quy +1

Counterfactual explanation (CE) is a widely used post-hoc method that provides individuals with actionable changes to alter an unfavorable prediction from a machine learning model.…

cs.LG2025

FACROC: a fairness measure for FAir Clustering through ROC curves

Tai Le Quy, Long Le Thanh, Lan Luong Thi Hong +1

Fair clustering has attracted remarkable attention from the research community. Many fairness measures for clustering have been proposed; however, they do not take into account the…