An explainable multi-attribute decision model based on argumentation - Xiuyi Fan (Expert Systems with Applications, 2019)
Originality:In this paper, we present a multi-attribute decision model and a method for explaining the decisions it recommends based on an argumentative reformulation of the model. Specifically, (i) we define a notion of best decisions amounting to achieving as many goals as possible and exhibiting as few redundant attributes as possible, and (ii) we generate explanations for why a decision is best or better than or as good as another.Significance:Explainability is important to automatic decision-making systems as without a clear understanding of how a recommended decision is generated, it is hard to ensure human trust, debug and improve these systems. By connecting decision making with argumentation, this work provides a mechanism for realizing explainable decision making. Rigor:All results are shown with full proofs. We also conduct an empirical evaluation of our method with legal practitioners, confirming that our method is helpful to understand automatically generated recommendations.