Usability, User Comprehension, and Perceptions of Explanations for Complex Decision Support Systems in Finance: A Robo-Advisory Use Case

Abstract

Robo-financial advisors are complex algorithmic decision-making systems with a high potential for mass adoption due to their low operating costs and multitasking abilities. The quantitative aspects of our study measure the efficacy and usability of explanations and qualitative aspects determine the effect of explanations on users and system usability.