Automated Team Formation is becoming increasingly important for a plethora of applications in open source community projects, remote working platforms, as well as online educational systems. The latter case, in particular, poses significant challenges that are specific to the educational domain. Indeed, teaming students aims to accomplish far more than the successful completion of a specific task. It needs to ensure that all members in the team benefit from the collaborative work, while also ensuring that the participants are not discriminated with respect to their protected attributes, such as race and gender. Towards achieving these goals, this work introduces FERN, a fair team formation approach that promotes mutually beneficial peer learning, dictated by protected group fairness as equality of opportunity in collaborative learning. We formulate the problem as a multi-objective discrete optimization problem. We show this problem to be NP-hard and propose a heuristic hill-climbing algorithm. Extensive experiments on both synthetic and real-world datasets against well-known team formation techniques show the effectiveness of the proposed method.

中文翻译：

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FERN：建立公平的团队以实现互利的合作学习

在开源社区项目，远程工作平台以及在线教育系统中的大量应用程序中，自动化团队形成正变得越来越重要。后一种情况尤其带来了教育领域特有的重大挑战。确实，成组学生的目标是完成成功任务比完成特定任务要远得多。它需要确保团队中的所有成员都能从协作工作中受益，同时还要确保参与者在其受保护的属性（例如种族和性别）方面不受歧视。为了实现这些目标，这项工作引入了FERN，这是一种公平的团队形成方法，可促进互惠互利的同伴学习，这是受保护的群体公平性要求的，即协作学习中的机会平等。我们将该问题表述为多目标离散优化问题。我们证明这个问题是NP难的，并提出了一种启发式爬山算法。在合成数据集和真实数据集上针对著名的团队形成技术进行的大量实验证明了该方法的有效性。