Rank aggregation algorithms for fair consensus
Rank aggregation algorithms for fair consensus
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DOI：
10.14778/3407790.3407855
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发表时间：
2020-07
影响因子：
2.5
通讯作者：
C. Kuhlman;Elke A. Rundensteiner
中科院分区：
文献类型：
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作者：
C. Kuhlman;Elke A. Rundensteiner
聚合数据库中的多个排名是数据库社区深入研究的一项重要任务。高风险的应用领域包括招聘、贷款和教育，多个决策者对候选人进行排名，然后将他们的意见合并为总体共识排名。虽然最先进的聚合方法成功地为每个决策者提供了平等的发言权，但迄今为止，还没有任何方法可以确保被排名的候选人群体的公平代表性，这些候选人可能会受到歧视性偏见的影响。我们针对这个悬而未决的问题提出了第一个解决方案，即保证弱势候选人群体的公平性，同时仍然对基本排名达成良好的共识。我们设计了一系列精确公平算法，实现公平排名聚合的最优性。我们还开发了近似方法，以保证对排名中的数百万候选人进行最小近似误差缩放，从而实现公平性。一项比较研究评估了我们提出的方法，揭示了在各种排名聚合场景中聚合准确性和不同程度的不公平偏见之间的权衡。我们的真实案例研究表明，我们的解决方案使用真实世界数据减轻了不公平偏见。
Aggregating multiple rankings in a database is an important task well studied by the database community. High-stakes application domains include hiring, lending, and education where multiple decision makers rank candidates and their input is then combined into an overall consensus ranking. While state-of-art aggregation methods succeed in giving equal say to each decision maker, to date no methods ensure fair representation for groups of candidates being ranked, who risk being impacted by discriminatory bias. We present the first solution to this open problem of guaranteeing fairness for disadvantaged groups of candidates, while still producing a good consensus of the base rankings. We design a family of exact fair algorithms achieving optimality for fair rank aggregation. We also develop approximate methods achieving fairness with guaranteed minimal approximation error scaling to millions of candidates in the rankings. A comparative study evaluates our proposed methods, revealing trade-offs between aggregation accuracy and different degrees of unfair bias in a rich variety of rank aggregation scenarios. Our real-world case study demonstrates that our solutions mitigate unfair bias using real-world data.