FERN：公平的团队形成，互利的协作学习
FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning
摘要：
对于开源社区项目、远程工作平台以及在线教育系统中的大量应用程序来说，自动化团队组建变得越来越重要。后一种情况尤其对教育领域提出了重大挑战。事实上，学生团队合作的目的远远超过成功完成一项具体任务。它需要确保团队中的所有成员都能从协作中受益，同时确保参与者不因其受保护的属性而受到歧视，如种族和性别。为了实现这些目标，这项工作介绍了FERN，公平的团队形成的方法，促进互利的同伴学习，受保护的群体公平的机会平等的协作学习。我们制定的问题作为一个多目标离散优化问题。我们证明了这个问题是NP难的，并提出了一个启发式爬山算法。在合成数据集和真实数据集上针对知名团队组建技术进行的大量实验表明了该方法的有效性。
英文摘要：
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.
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