Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation

2025-09-10

AI Summary

This paper challenges the appropriateness of envy-freeness up to one item (EF-1) as a fairness metric for personalized recommendation systems. It identifies EF-1’s fundamental limitations in settings with highly heterogeneous user preferences—namely, its inability to prevent substantial utility deprivation, resource allocation imbalance, or group-level bias, even when satisfied. Method: Drawing on insights from economics, game theory, and recommender systems theory, the authors conduct conceptual analysis and construct counterexamples to expose EF-1’s inadequacy. Contribution/Results: The work demonstrates that EF-1 only guarantees pairwise, ordinal, local fairness and fails to capture multidimensional fairness requirements inherent in personalization—such as representativeness, accessibility, and long-term welfare. Consequently, the paper advocates moving beyond static, ordinal fairness criteria toward a dynamic, multi-objective fairness framework tailored to the recommendation paradigm. This contributes to a foundational reexamination and reconstruction of fairness theory in recommender systems.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Envy-freeness and the relaxation to Envy-freeness up to one item (EF-1) have been used as fairness concepts in the economics, game theory, and social choice literatures since the 1960s, and have recently gained popularity within the recommendation systems communities. In this short position paper we will give an overview of envy-freeness and its use in economics and recommendation systems; and illustrate why envy is not appropriate to measure fairness for use in settings where personalization plays a role.

Problem

• Evaluating envy-freeness fairness in personalized recommendation systems
• Questioning EF-1 appropriateness for personalized settings
• Analyzing fairness metrics mismatch in recommendation contexts

Innovation

• EF-1 fairness concept application
• Personalized recommendation systems analysis
• Envy-freeness limitations evaluation