Building Contrastive Explanations for Multi-Agent Team Formation

As more and more hard and complex procedures are being automated with the aid of artificial intelligence, the need for humans to understand the rationale behind AI decisions becomes imperative. Adequate explanations for decisions made by an intelligent system do not just help describing how the system works, they also earn users' trust. In this work we focus on a general methodology for justifying why certain teams are formed and others are not by a team formation algorithm (TFA). Specifically, we introduce an algorithm that wraps up any existing TFA and builds justifications regarding the teams formed by such TFA. This is done without modifying the TFA in any way. Our algorithm offers users a collection of commonly-asked questions within a team formation scenario and builds justifications as contrastive explanations. We also report on an empirical evaluation to determine the quality of the explanations provided by our algorithm.