Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation
Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation
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DOI：
10.1145/3593013.3594085
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发表时间：
2023-06
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影响因子：
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通讯作者：
Kathleen Cachel;Elke A. Rundensteiner
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作者：
Kathleen Cachel;Elke A. Rundensteiner
在社会选择中，传统的凯门尼（Kemeny）等级聚合将选民的偏好（表示为排名）结合在一起，将其排名为单一的共识排名，而无需考虑这种排名如何不公平地影响边缘化群体（即种族或性别）。开发公平等级的聚合方法至关重要，因为它们在优先申请求职者，资助建议和安排医学患者的申请中的社会影响力。在这项工作中，我们介绍了公平的接触Kemeny聚集问题（公平），以将庞大和多样化的选民偏好相结合为一个不仅是合适的共识的单一排名，而且可以确保边缘化群体没有利用机会。在正式化FairExp-KAP时，我们将暴露概念的公平性从信息检索扩展到等级聚合环境，并为选民偏好代表提供了免费度量。我们设计用于解决位置偏见的算法来解决公平exp-kap，这是一种基于排名的关注，最终用户更加关注排名更高的候选人。 EPIK通过将曝光的非对方公平性纳入成对的Kemeny优化来解决FairExp-KAP。虽然大约Epira是候选人交换算法，但保证了候选人的公平性。利用全面的合成模拟和六个现实世界数据集，我们表明了我们的方法的功效，表明我们成功地减轻了共识排名中不同的群体暴露不公平，而最大程度地代表了选民偏好。
In social choice, traditional Kemeny rank aggregation combines the preferences of voters, expressed as rankings, into a single consensus ranking without consideration for how this ranking may unfairly affect marginalized groups (i.e., racial or gender). Developing fair rank aggregation methods is critical due to their societal influence in applications prioritizing job applicants, funding proposals, and scheduling medical patients. In this work, we introduce the Fair Exposure Kemeny Aggregation Problem (FairExp-kap) for combining vast and diverse voter preferences into a single ranking that is not only a suitable consensus, but ensures opportunities are not withheld from marginalized groups. In formalizing FairExp-kap, we extend the fairness of exposure notion from information retrieval to the rank aggregation context and present a complimentary metric for voter preference representation. We design algorithms for solving FairExp-kap that explicitly account for position bias, a common ranking-based concern that end-users pay more attention to higher ranked candidates. epik solves FairExp-kap exactly by incorporating non-pairwise fairness of exposure into the pairwise Kemeny optimization; while the approximate epira is a candidate swapping algorithm, that guarantees ranked candidate fairness. Utilizing comprehensive synthetic simulations and six real-world datasets, we show the efficacy of our approach illustrating that we succeed in mitigating disparate group exposure unfairness in consensus rankings, while maximally representing voter preferences.