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: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 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.