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.

翻译：团体组建的目标是建立一支团队,以完成具体任务; 使用算术来提高以这种方式组成的团队的有效性和群体遴选过程的效率; 然而,人们担心团队组建算法会由于算法本身或他们接受培训所依据的数据而对少数群体产生偏向; 因此,必须建立公平的团队组建系统,将人口信息纳入团队建设过程; 尽管在为专家建议和或团队组建建立的个人专门知识建模方面做了大量工作,但先前关于人口统计建模和将人口统计纳入团队组建过程的工作相对较少; 我们提出了一个基于多层面人口特征代表专家人口概况的新颖方法; 此外,我们引入了两种多样性排序算法,通过考虑人口特征和最低所需技能组成一个群体; 与许多考虑布奥利安人口特征(例如性别或种族)的排名算法不同,我们的多样性排序算法同时考虑多个多值人口属性; 我们利用基于计算机科学方案委员会成员的真实数据配置来评估我们拟议的算法。 结果显示,我们的拟议算法形成了一种可接受的通用性损失。