  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.
