Multi-Objective Ensemble Machine Learning for Fairness

Abstract

With the proliferation of machine learning applications across industries such as hiring, finance, surveillance, and healthcare, concerns about the fairness and equity of artificial intelligence (AI) have intensified. Recent incidents highlighting biased AI predictions have underscored the urgent need to ensure fairness in these systems. This paper introduces Multi-Objective Ensemble Learning for Fairness (MELF), a novel approach that combines ensemble learning and multi-objective decision-making to train machine learning models that achieve a balance between predictive performance and fairness metrics. MELF is adaptable across various datasets and machine learning algorithms and can be integrated with other fairness-aware training techniques. Computational experiments with decision trees and logistic regression machine learning algorithms demonstrate that MELF can enhance fairness without compromising predictive accuracy.