Analyzing Data Bias through Data Augmentation: A Case Study in Financial Data - Porphirio, Julia dos Santos; Santos, Diogo José dos; Azevedo, Sérgio; Berton, Lilian (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This study examines the impact of introducing group unbalances through oversampling strategies on model fairness and feature importance, especially concerning sensitive attributes like sex, marital status, and education in a financial dataset. We hypothesize that linear models are more vulnerable to fairness distortions introduced by oversampling generated by synthetic data generation than more complex models, such as gradient-boosted decision trees and support vector machines with an RBF kernel. To test this, we evaluate oversampling approaches like SMOTE and RandomOverSampler within a consistent framework, comparing linear classifiers against XGBoost and SVM (RBF). Our assessment includes predictive performance, the stability of fairness metrics, and changes in feature importance rankings before and after oversampling.