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Task allocation is an important problem in multi-agent systems. It becomesmore challenging when the team-members are humans with imperfect knowledgeabout their teammates' costs and the overall performance metric. In this paper,we propose a centralized Artificial Intelligence Task Allocation (AITA) thatsimulates a negotiation and produces a negotiation-aware explicable taskallocation. If a team-member is unhappy with the proposed allocation, we allowthem to question the proposed allocation using a counterfactual. By using partsof the simulated negotiation, we are able to provide contrastive explanationsthat provide minimum information about other's cost to refute their foil. Withhuman studies, we show that (1) the allocation proposed using our methodappears fair to the majority, and (2) when a counterfactual is raised,explanations generated are easy to comprehend and convincing. Finally, weempirically study the effect of different kinds of incompleteness on theexplanation-length and find that underestimation of a teammate's costs oftenincreases it.

`Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation