Year: 2024
Type: conference-paper
Abstract: This paper presents a comprehensive analysis of fairness in machine learning models using the UCI Adult Dataset. The study focuses on mitigating biases related to sensitive attributes such as race and gender by reducing the dimensionality of the dataset. We evaluated the performance and fairness of three popular machine learning models—Logistic Regression, Random Forest, and Gradient Boosting—both with and without including sensitive features. The results indicate that while performance metrics remain stable, the fairness metrics reveal significant insights, underscoring the necessity of considering fairness alongside performance in machine learning applications.
Authors: Lucas Sena, Javam Machado
Institution: Universidade Federal do Ceará
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: gold
Award: G612945618
OpenAlex ID: W4403824319