Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF

• URL: http://arxiv.org/abs/2309.08621v2
• Date: Thu, 5 Oct 2023 16:07:59 GMT
• Title: Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
• Authors: Amanda Aird, Cassidy All, Paresha Farastu, Elena Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke
• Abstract summary: A social choice formulation of the fairness problem offers a flexible and multi-aspect alternative to fairness-aware recommendations. We show that different classes of choice and allocation mechanisms yield different but consistent fairness / accuracy tradeoffs.
• Score: 11.43931298398417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations. A social choice formulation of the fairness problem, operating within a multi-agent architecture of fairness concerns, offers a flexible and multi-aspect alternative to fairness-aware recommendation approaches. Leveraging social choice allows for increased generality and the possibility of tapping into well-studied social choice algorithms for resolving the tension between multiple, competing fairness concerns. This paper explores a range of options for choice mechanisms in multi-aspect fairness applications using both real and synthetic data and shows that different classes of choice and allocation mechanisms yield different but consistent fairness / accuracy tradeoffs. We also show that a multi-agent formulation offers flexibility in adapting to user population dynamics.

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