Fairness Considerations for Enhanced Team Collaboration

Fairness plays an important role in decision-making within teams and its perception has shown to drive performance and individual behavior among team members. Robots deployed within human teams are consistently faced with decisions on how to optimally allocate resources (e.g. tools, attention, gaze) but current solutions often ignore key aspects of fairness. In this work, we look to leverage laboratory experiments to identify key performance and behavioral metrics to further develop algorithmic solutions that include fairness considerations. We look to the well established multi-armed bandit algorithms to frame our problem and establish constraints on how resources are distributed amongst team members.