基于多种人口统计特征的多样化群体形成
Diverse Group Formation Based on Multiple Demographic Features
摘要：
小组形成的目标是建立一个团队来完成一项特定的任务。算法被用来提高这样形成的团队的有效性和组选择过程的效率。然而，人们担心，由于算法本身或训练数据的原因，团队形成算法可能会对少数群体产生偏见。因此，必须建立公平的团队组建系统，将人口统计信息纳入团队建设过程。虽然已经有大量的工作，为专家推荐和/或团队形成的个人专业知识建模，有相对较少的人口统计建模和人口统计纳入组形成过程中的工作。提出了一种基于多维人口统计特征的专家人口统计特征表示方法。此外，我们引入了两种多样性排名算法，通过考虑人口统计特征以及最低所需技能来形成一个组。与考虑一个布尔人口统计特征的许多排名算法（例如，性别或种族），我们的多样性排名算法同时考虑多个多值人口统计属性。我们评估我们提出的算法使用一个真正的数据集的基础上的成员的计算机科学程序委员会。结果表明，我们的算法形成了一个程序委员会，更多样化的实用程序的损失是可以接受的。
英文摘要：
The goal of group formation is to build a team to accomplish a specific task. Algorithms are employed to improve the effectiveness of the team so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals expertise for expert recommendation and or team formation, there has been relatively little prior work on modeling demographics and incorporating demographics into the group formation process. We propose a novel method to represent experts demographic profiles based on multidimensional demographic features. Moreover, we introduce two diversity ranking algorithms that form a group by considering demographic features along with the minimum required skills. Unlike many ranking algorithms that consider one Boolean demographic feature (e.g., gender or race), our diversity ranking algorithms consider multiple multivalued demographic attributes simultaneously. We evaluate our proposed algorithms using a real dataset based on members of a computer science program committee. The result shows that our algorithms form a program committee that is more diverse with an acceptable loss in utility.
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