# Who Pays? Personalization, Bossiness and the Cost of Fairness

arXiv: https://arxiv.org/abs/2209.04043 · HTML: https://mlsanity.com/paper/2209.04043/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [Selection Problems in the Presence of Implicit Bias](https://mlsanity.com/paper/1801.03533/) (2018)
- [CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://mlsanity.com/paper/2204.08085/) (2022)
- [Two-sided fairness in rankings via Lorenz dominance](https://mlsanity.com/paper/2110.15781/) (2021)
- [Opportunistic Multi-aspect Fairness through Personalized Re-ranking](https://mlsanity.com/paper/2005.12974/) (2020)
- ["And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation](https://mlsanity.com/paper/2009.02590/) (2020)
- [Relating Metric Distortion and Fairness of Social Choice Rules](https://mlsanity.com/paper/1810.01092/) (2018)
- [Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes](https://mlsanity.com/paper/1703.00320/) (2017)

## Built on by (most cited first, 2 of 2)

- [Dynamic fairness-aware recommendation through multi-agent social choice](https://mlsanity.com/paper/2303.00968/) (2023)
- [Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation](https://mlsanity.com/paper/2509.09037/) (2025)

## Abstract (arXiv)

Fairness-aware recommender systems that have a provider-side fairness concern seek to ensure that protected group(s) of providers have a fair opportunity to promote their items or products. There is a ``cost of fairness'' borne by the consumer side of the interaction when such a solution is implemented. This consumer-side cost raises its own questions of fairness, particularly when personalization is used to control the impact of the fairness constraint. In adopting a personalized approach to the fairness objective, researchers may be opening their systems up to strategic behavior on the part of users. This type of incentive has been studied in the computational social choice literature under the terminology of ``bossiness''. The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others. This position paper introduces the concept of bossiness, shows its application in fairness-aware recommendation and discusses strategies for reducing this strategic incentive.