Team assembly is a problem that demands trade-offs between multiple fairness criteria and computational optimization. We focus on four criteria: (i) fair distribution of workloads within the team, (ii) fair distribution of skills and expertise regarding project requirements, (iii) fair distribution of protected classes in the team, and (iv) fair distribution of the team cost among protected classes. For this problem, we propose a two-stage algorithmic solution. First, a multi-objective optimization procedure is executed and the Pareto candidates that satisfy the project requirements are selected. Second, N random groups are formed containing combinations of these candidates, and a second round of multi-objective optimization is executed, but this time for selecting the groups that optimize the team-assembly criteria. We also discuss the conflicts between those objectives when trying to understand the impact of fairness constraints in the utility associated with the formed team.

翻译：团队组建是一个需要在多重公平性标准与计算优化之间进行权衡的问题。我们聚焦于四项标准：（i）团队内部工作量的公平分配，（ii）项目所需技能与专业知识的公平分配，（iii）团队中受保护类别的公平分布，以及（iv）受保护类别间团队成本的公平分配。针对该问题，我们提出了一种两阶段算法解决方案。首先，执行多目标优化流程，筛选出满足项目需求的帕累托候选解。其次，随机生成N个由这些候选解组合而成的候选组，并执行第二轮多目标优化——但此次旨在选取能够优化团队组建标准的组群。我们还讨论了这些目标之间的冲突，以深入理解公平性约束对已组建团队效用的影响。