Assessing Demand for Transparency in Intelligent Systems Using Machine Learning

Abstract

Intelligent systems offering decision support can lessen cognitive load and improve the efficiency of decision making in a variety of contexts. These systems assist users by evaluating multiple courses of action and recommending the right action at the right time. Modern intelligent systems using machine learning introduce new capabilities in decision support, but they can come at a cost. Machine learning models provide little explanation of their outputs or reasoning process, making it difficult to determine when it is appropriate to trust, or if not, what went wrong. In order to improve trust and ensure appropriate reliance on these systems, users must be afforded increased transparency, enabling an understanding of the systems reasoning, and an explanation of its predictions or classifications. Here we discuss the salient factors in designing transparent intelligent systems using machine learning, and present the results of a user-centered design study. We propose design guidelines derived from our study, and discuss next steps for designing for intelligent system transparency.