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Multi-agent Social Choice for Dynamic Fairness-aware Recommendation

Authors:

Nicholas Mattei,

Vladislav Grozin,

Nasim SonboliAuthors Info & Claims

UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

Pages 234 - 244

https://doi.org/10.1145/3511047.3538032

Published: 04 July 2022 Publication History

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Abstract

The pursuit of algorithmic fairness requires that we think differently about the idea of the “user” in personalized systems, such as recommender systems. The conventional definition of the user in such systems focuses on the receiver of recommendations, the individual to whom a particular personalization output is directed. Fairness, especially provider-side fairness, requires that we consider a broader array of system users and stakeholders, whose needs, interests and preferences may need to be modeled. In this paper, we describe a framework in which the interests of providers and other stakeholders are represented as agents. These agents participate in the production of recommendations through a two-stage social choice mechanism. This approach has the benefit of being able to represent a wide variety of fairness concepts and to extend to multiple fairness concerns.

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Cited By

Gómez EContreras DSalamo MBoratto L(2024)Bringing Equity to Coarse and Fine-Grained Provider Groups in Recommender SystemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659552(18-23)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659552
Banerjee ABanik PWörndl W(2023)A review on individual and multistakeholder fairness in tourism recommender systemsFrontiers in Big Data10.3389/fdata.2023.11686926Online publication date: 10-May-2023
https://doi.org/10.3389/fdata.2023.1168692
Smith JBuhayh AKathait ARagothaman PMattei NBurke RVoida A(2023)The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending RecommendationProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594106(1652-1663)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594106
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Published In

cover image ACM Conferences

UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

July 2022

409 pages

ISBN:9781450392327

DOI:10.1145/3511047

General Chairs:
Alejandro Bellogin
Universidad Autonoma de Madrid, Spain
,
Ludovico Boratto
University of Cagliari, Italy
,
Olga C. Santos
UNED, Spain
,
Program Chairs:
Liliana Ardissono
Universita di Torino, Italy
,
Bart Knijnenburg
Clemson University, United States

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This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 04 July 2022

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UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization

July 4 - 7, 2022

Barcelona, Spain

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Cited By

Gómez EContreras DSalamo MBoratto L(2024)Bringing Equity to Coarse and Fine-Grained Provider Groups in Recommender SystemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659552(18-23)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659552
Banerjee ABanik PWörndl W(2023)A review on individual and multistakeholder fairness in tourism recommender systemsFrontiers in Big Data10.3389/fdata.2023.11686926Online publication date: 10-May-2023
https://doi.org/10.3389/fdata.2023.1168692
Smith JBuhayh AKathait ARagothaman PMattei NBurke RVoida A(2023)The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending RecommendationProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594106(1652-1663)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594106
Musick GHauptman AFlathmann CMcNeese NKnijnenburg B(2023)Recommendations with Benefits: Exploring Explanations in Information Sharing Recommender Systems for Temporary TeamsInternational Journal of Human–Computer Interaction10.1080/10447318.2023.2278933(1-17)Online publication date: 20-Nov-2023
https://doi.org/10.1080/10447318.2023.2278933
Briman ETalmon N(2023)Multiple Attribute List Aggregation and an Application to Democratic Playlist EditingMulti-Agent Systems10.1007/978-3-031-43264-4_1(1-16)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43264-4_1

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