Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics

2025-04-21

AI Summary

Prior work lacks a theoretical foundation for the inherent trade-off between accuracy and item fairness in ranking re-ranking. Method: This paper introduces the economic concept of “elasticity” to formally model the sensitivity of fairness to changes in accuracy as a utility elasticity—drawing an analogy to tax incidence theory—and proposes the Elasticity-Fairness Curve (EF-Curve) as a novel evaluation framework. It further designs ElasticRank, the first re-ranking algorithm that dynamically adjusts inter-item distances based on elasticity estimates. Contribution/Results: Extensive experiments on three benchmark ranking datasets demonstrate that ElasticRank significantly improves the joint optimization of fairness and accuracy across diverse elasticity regimes. The approach advances fair ranking from empirical hyperparameter tuning toward an interpretable, quantifiable, economics-inspired modeling paradigm.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Fairness is an increasingly important factor in re-ranking tasks. Prior work has identified a trade-off between ranking accuracy and item fairness. However, the underlying mechanisms are still not fully understood. An analogy can be drawn between re-ranking and the dynamics of economic transactions. The accuracy-fairness trade-off parallels the coupling of the commodity tax transfer process. Fairness considerations in re-ranking, similar to a commodity tax on suppliers, ultimately translate into a cost passed on to consumers. Analogously, item-side fairness constraints result in a decline in user-side accuracy. In economics, the extent to which commodity tax on the supplier (item fairness) transfers to commodity tax on users (accuracy loss) is formalized using the notion of elasticity. The re-ranking fairness-accuracy trade-off is similarly governed by the elasticity of utility between item groups. This insight underscores the limitations of current fair re-ranking evaluations, which often rely solely on a single fairness metric, hindering comprehensive assessment of fair re-ranking algorithms. Centered around the concept of elasticity, this work presents two significant contributions. We introduce the Elastic Fairness Curve (EF-Curve) as an evaluation framework. This framework enables a comparative analysis of algorithm performance across different elasticity levels, facilitating the selection of the most suitable approach. Furthermore, we propose ElasticRank, a fair re-ranking algorithm that employs elasticity calculations to adjust inter-item distances within a curved space. Experiments on three widely used ranking datasets demonstrate its effectiveness and efficiency.

Problem

• Explores accuracy-fairness trade-offs in re-ranking using economic elasticity
• Proposes Elastic Fairness Curve to evaluate algorithm performance comprehensively
• Introduces ElasticRank algorithm to balance fairness and accuracy adaptively

Innovation

• Elastic Fairness Curve for evaluation framework
• ElasticRank algorithm adjusts inter-item distances
• Uses elasticity to balance accuracy-fairness trade-offs