# Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF

arXiv: https://arxiv.org/abs/2309.08621 · HTML: https://mlsanity.com/paper/2309.08621/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Towards Long-term Fairness in Recommendation](https://mlsanity.com/paper/2101.03584/) (2021)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [Evaluating Stochastic Rankings with Expected Exposure](https://mlsanity.com/paper/2004.13157/) (2020)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Opportunistic Multi-aspect Fairness through Personalized Re-ranking](https://mlsanity.com/paper/2005.12974/) (2020)
- [Personalizing Fairness-aware Re-ranking](https://mlsanity.com/paper/1809.02921/) (2018)
- [Dynamic fairness-aware recommendation through multi-agent social choice](https://mlsanity.com/paper/2303.00968/) (2023)
- [The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods](https://mlsanity.com/paper/1705.06840/) (2017)
- ["And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation](https://mlsanity.com/paper/2009.02590/) (2020)

## Built on by (most cited first, 1 of 1)

- [Aggregating User Preferences while Ensuring Equity, Diversity, and Inclusion using Graph Summarization](https://mlsanity.com/paper/2610.07128/) (2026)

## Abstract (arXiv)

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.