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.

翻译：团队组建是一个需要在多重公平性准则与计算优化之间进行权衡的问题。我们聚焦于四项准则：(i) 团队内工作量的公平分配，(ii) 技能和专业知识与项目需求的公平匹配，(iii) 团队中受保护类别的公平分布，以及 (iv) 团队成本在受保护类别间的公平分配。针对此问题，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方案。其次，随机生成包含这些候选方案组合的N个团队，并再次执行多目标优化流程，但此次旨在筛选出能优化团队组建准则的团队组合。