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

Fuente: arXiv
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Main Authors: Aird, Amanda, Armstrong, Ben, Mattei, Nicholas, Burke, Robin
Format: Preprint
Published: 2025
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author Aird, Amanda
Armstrong, Ben
Mattei, Nicholas
Burke, Robin
author_facet Aird, Amanda
Armstrong, Ben
Mattei, Nicholas
Burke, Robin
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2509_09037
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation
Aird, Amanda
Armstrong, Ben
Mattei, Nicholas
Burke, Robin
Information Retrieval
Artificial Intelligence
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.
title Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation
topic Information Retrieval
Artificial Intelligence
url https://arxiv.org/abs/2509.09037