[2503.11120] A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems

Computer Science > Machine Learning arXiv:2503.11120 (cs) [Submitted on 14 Mar 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems Authors:Gökhan Özbulak, Oscar Jimenez-del-Toro, Maíra Fatoretto, Lilian Berton, André Anjos View a PDF of the paper titled A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems, by G\"okhan \"Ozbulak and Oscar Jimenez-del-Toro and Ma\'ira Fatoretto and Lilian Berton and Andr\'e Anjos View PDF HTML (experimental) Abstract:The evaluation of fairness models in Machine Learning involves complex challenges, such as defining appropriate metrics, balancing trade-offs between utility and fairness, and there are still gaps in this stage. This work presents a novel multi-objective evaluation framework that enables the analysis of utility-fairness trade-offs in Machine Learning systems. The framework was developed using criteria from Multi-Objective Optimization that collect comprehensive information regarding this complex evaluation task. The assessment of multiple Machine Learning systems is summarized, both quantitatively and qualitatively, in a straightforward manner through a radar chart and a measurement table encompassing various aspects such as convergence, system capacity, and diversity. The framework's compact representation of performance facilitates the comparative analysi...