In this study, we examined the impact of recommendation systems' algorithms on individuals' collaborator choices when forming teams. Different algorithmic designs can lead individuals to select one collaborator over another, thereby shaping their teams' composition, dynamics, and performance. To test this hypothesis, we conducted a 2 x 2 between-subject laboratory experiment with 332 participants who assembled teams using a recommendation system. We tested four algorithms that controlled the participants' agency to choose collaborators and the inclusion of fairness criteria. Our results show that participants assigned by an algorithm to work in highly diverse teams struggled to work with different and unfamiliar individuals, while participants enabled by an algorithm to choose collaborators without fairness criteria formed homogenous teams without the necessary skills. In contrast, combining users' agency and fairness criteria in an algorithm enhanced teams' performance and composition. This study breaks new ground by providing insights into how algorithms can augment team formation.

翻译：本研究探讨了推荐系统算法对个体组建团队时选择合作者的影响。不同的算法设计会导致个体倾向于选择特定合作者，从而塑造团队的构成、动态与绩效。为验证这一假设，我们设计了一个2×2的组间实验室实验，332名参与者使用推荐系统组建团队。我们测试了四种算法，分别控制参与者选择合作者的自主权以及是否包含公平性准则。结果显示：被算法分配至高度多样化团队的参与者难以与不同且陌生的个体协作；而通过算法获得自主选择权但未受公平性准则约束的参与者则组建了同质化团队，缺乏必要的技能组合。相比之下，在算法中结合用户自主权与公平性准则能显著提升团队绩效并优化团队构成。本研究通过揭示算法增强团队组建的机制，开辟了新的研究路径。