Who Pays? Personalization, Bossiness and the Cost of Fairness
Not on the map. Why?
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.
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
11 of 28 references.
References · 11
- 2017Algorithmic decision making and the cost of fairnesscited by 403
- 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 78
- 2017Multisided Fairness for Recommendationcited by 70
- 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
- 2018Selection Problems in the Presence of Implicit Biascited by 29
- 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
- 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
- 2020Opportunistic Multi-aspect Fairness through Personalized Re-rankingcited by 11
- 2020"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendationcited by 5
- 2018Relating Metric Distortion and Fairness of Social Choice Rulescited by 4
- 2017Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudescited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.