Social Choice for Heterogeneous Fairness in Recommendation

• URL: http://arxiv.org/abs/2410.04551v1
• Date: Sun, 6 Oct 2024 17:01:18 GMT
• Title: Social Choice for Heterogeneous Fairness in Recommendation
• Authors: Amanda Aird, Elena Štefancová, Cassidy All, Amy Voida, Martin Homola, Nicholas Mattei, Robin Burke,
• Abstract summary: Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders. Previous work has often been limited by fixed, single-objective definitions of fairness. Our work approaches recommendation fairness from the standpoint of computational social choice.
• Score: 9.753088666705985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.

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