Investigating explanations to justify choice

Abstract

Many different forms of explanation have been proposed for justifying decisions made by automated systems. However, there is no consensus on what constitutes a good explanation, or what information these explanations should include. In this paper, we present the results of a study into how people justify their decisions. Analysis of our results allowed us to extract the forms of explanation adopted by users to justify choices, and the situations in which these forms are used. The analysis led to the development of guidelines and patterns for explanations to be generated by automated decision systems. This paper presents the study, its results, and the guidelines and patterns we derived. © 2012 Springer-Verlag.