Multi-Armed Bandits with Fairness Constraints for Distributing Resources
  to Human Teammates
  How should a robot that collaborates with multiple people decide upon the
distribution of resources (e.g. social attention, or parts needed for an
assembly)? People are uniquely attuned to how resources are distributed. A
decision to distribute more resources to one team member than another might be
perceived as unfair with potentially detrimental effects for trust. We
introduce a multi-armed bandit algorithm with fairness constraints, where a
robot distributes resources to human teammates of different skill levels. In
this problem, the robot does not know the skill level of each human teammate,
but learns it by observing their performance over time. We define fairness as a
constraint on the minimum rate that each human teammate is selected throughout
the task. We provide theoretical guarantees on performance and perform a
large-scale user study, where we adjust the level of fairness in our algorithm.
Results show that fairness in resource distribution has a significant effect on
users' trust in the system.
