Principles of Explanation in Human-AI Systems
This paper is significant for XAI developers and researchers, offering a framework to design and evaluate XAI systems based on human-centered principles, thereby addressing the current gap in user-centric explainability.
The paper addresses the lack of user-centered design in Explainable AI (XAI) by proposing a shift from algorithm-focused to human-focused principles. It introduces the "Self-Explanation Scorecard" and a set of empirically-grounded, user-centered design principles to guide the development of XAI systems.
Explainable Artificial Intelligence (XAI) has re-emerged in response to the development of modern AI and ML systems. These systems are complex and sometimes biased, but they nevertheless make decisions that impact our lives. XAI systems are frequently algorithm-focused; starting and ending with an algorithm that implements a basic untested idea about explainability. These systems are often not tested to determine whether the algorithm helps users accomplish any goals, and so their explainability remains unproven. We propose an alternative: to start with human-focused principles for the design, testing, and implementation of XAI systems, and implement algorithms to serve that purpose. In this paper, we review some of the basic concepts that have been used for user-centered XAI systems over the past 40 years of research. Based on these, we describe the "Self-Explanation Scorecard", which can help developers understand how they can empower users by enabling self-explanation. Finally, we present a set of empirically-grounded, user-centered design principles that may guide developers to create successful explainable systems.