What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Abstract

Explainability is a hot topic nowadays for artificial intelligent (AI) systems. The role of machine learning (ML) models on influencing human decisions shed light on the back-box of computing systems. AI based system are more than just ML models. ML models are one element for the AI explainability’ design and needs to be combined with other elements so it can have significant meaning for people using AI systems. There are different goals and motivations for AI explainability. Regardless the goal for AI explainability, there are more to AI explanation than just ML models or algorithms. The explainability of an AI systems behavior needs to consider different dimensions: 1) who is the receiver of that explanation, 2) why that explanation is needed, and 3) in which context and other situated information the explanation is presented. Considering those three dimensions, the explanation can be effective by fitting the user needs and expectation in the right moment and format. The design of an AI explanation user experience is central for the pressing need from people and the society to understand how an AI system may impact on human decisions. In this paper, we present a literature review on AI explainability research and practices. We first looked at the computer science (CS) community research to identify the main research themes about AI explainability, or “explainable AI”. Then, we focus on Human-Computer Interaction (HCI) research trying to answer three questions about the selected publications: to whom the AI explainability is for (who), which is the purpose of the AI explanation (why), and in which context the AI explanation is presented (what + when).