Who Pays? Personalization, Bossiness and the Cost of Fairness
Who Pays? Personalization, Bossiness and the Cost of Fairness
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谁付钱？
DOI：
10.48550/arxiv.2209.04043
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
R. Burke
中科院分区：
文献类型：
--
作者：
Paresha Farastu;Nicholas Mattei;R. Burke
具有提供者端公平性考量的公平感知推荐系统旨在确保受保护的提供者群体有公平的机会来推广他们的物品或产品。当实施这样的解决方案时，交互的消费者端会承担“公平成本”。这种消费者端成本引发了其自身的公平性问题，特别是当使用个性化来控制公平性约束的影响时。在对公平目标采用个性化方法时，研究人员可能会使他们的系统面临用户的策略性行为。这种激励类型在计算社会选择文献中已在“专横”这一术语下进行了研究。令人担忧的是，一个专横的用户可能能够将公平成本转嫁给他人，改善自己的结果而使他人的结果变差。这篇立场文件介绍了专横的概念，展示了它在公平感知推荐中的应用，并讨论了减少这种策略性激励的策略。
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.
将度量扭曲与社会选择规则的公平性联系起来
DOI：
10.1145/3230654.3230658
发表时间：
2018
期刊：
Systems and Computation
影响因子：
--
作者：
Goel, Ashish;Hulett, Reyna;Krishnaswamy, Anilesh K.
通讯作者：
Krishnaswamy, Anilesh K.
通过个性化重新排名实现机会主义多方面公平
DOI：
10.1145/3340631.3394846
发表时间：
2020
期刊：
Adaptation and Personalization (UMAP
影响因子：
--
作者：
Sonboli, Nasim;Eskandanian, Farzad;Burke, Robin;Liu, Weiwen;Mobasher, Bamshad
通讯作者：
Mobasher, Bamshad