Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916139067506688 |
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| author | Aird, Amanda All, Cassidy Farastu, Paresha Stefancova, Elena Sun, Joshua Mattei, Nicholas Burke, Robin |
| author_facet | Aird, Amanda All, Cassidy Farastu, Paresha Stefancova, Elena Sun, Joshua Mattei, Nicholas Burke, Robin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08621 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF Aird, Amanda All, Cassidy Farastu, Paresha Stefancova, Elena Sun, Joshua Mattei, Nicholas Burke, Robin Information Retrieval Artificial Intelligence 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. |
| title | Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2309.08621 |