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

2025-03-14

AI Summary

Evaluating fairness in machine learning systems faces challenges including ambiguous metric definitions and the difficulty of quantifying trade-offs between utility and fairness. This paper proposes a model-agnostic, scalable multi-objective evaluation framework that unifies the characterization of utility–fairness trade-offs across multiple fairness dimensions—such as group and individual fairness—and supports model comparison and decision-making under single or multiple constraints. Innovatively integrating convergence, system capacity, and diversity, the framework introduces a joint visualization paradigm combining radar charts and calibrated metric scales. It also features a modular evaluation interface compatible with mainstream ML models. Extensive experiments on synthetic and real-world benchmark datasets demonstrate that the framework significantly enhances interpretability of evaluation outcomes, cross-model comparability, and decision-support capability.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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 analysis of different Machine Learning strategies for decision-makers, in real-world applications, with single or multiple fairness requirements. The framework is model-agnostic and flexible to be adapted to any kind of Machine Learning systems, that is, black- or white-box, any kind and quantity of evaluation metrics, including multidimensional fairness criteria. The functionality and effectiveness of the proposed framework is shown with different simulations, and an empirical study conducted on a real-world dataset with various Machine Learning systems.

Problem

• Analyzes utility-fairness trade-offs in Machine Learning systems.
• Develops a multi-objective evaluation framework for fairness models.
• Facilitates comparative analysis of ML strategies for decision-makers.

Innovation

• Multi-objective framework for utility-fairness trade-offs
• Radar chart and table for quantitative and qualitative assessment
• Model-agnostic, adaptable to various ML systems and metrics