Augmenting team diversity and performance by enabling agency and fairness criteria in recommendation algorithms

TL;DR

This paper investigates how recommendation algorithms influence team formation, showing that combining user agency with fairness criteria improves team diversity, composition, and performance in collaborative settings.

Contribution

It introduces a novel approach integrating agency and fairness in recommendation algorithms to enhance team diversity and effectiveness.

Findings

Highly diverse teams formed by algorithms with agency faced collaboration challenges.

Homogeneous teams formed without fairness criteria lacked necessary skills.

Combining agency and fairness criteria led to better team performance and composition.

Abstract

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.