Decision Referrals in Human-Automation Teams

We consider a model for optimal decision referrals in human-automation teams performing binary classification tasks. The automation observes a batch of independent tasks, analyzes them, and has the option to refer a subset of them to a human operator. The human operator performs fresh analysis of the tasks referred to him. Our key modeling assumption is that the human performance degrades with workload (i.e., the number of tasks referred to human). We model the problem as a stochastic optimization problem. We first consider the special case when the workload of the human is pre-specified. We show that in this setting it is optimal to myopically refer tasks which lead to the largest reduction in the conditional expected cost until the desired workload target is met. We next consider the general setting where there is no constraint on the workload. We leverage the solution of the previous step and provide a search algorithm to efficiently find the optimal set of tasks to refer. Finally, we present a numerical study to compare the performance of our algorithm with some baseline allocation policies.