Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911149227769856 |
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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 |
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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 |