Explainability scenarios: towards scenario-based XAI design

Abstract

Integral to the adoption and uptake of AI systems in real-world settings is the ability for people to make sense of and evaluate such systems, a growing area of development and design efforts known as XAI (Explainable AI). Recent work has advanced the state of the art, yet a key challenge remains in understanding unique requirements that might arise when XAI systems are deployed into complex settings of use. In helping envision such requirements, this paper turns to scenario-based design, a method that anticipates and leverages scenarios of possible use early on in system development. To demonstrate the value of the scenario-based design method to XAI design, this paper presents a case study of aging-in-place monitoring. Introducing the concept of "explainability scenarios" as resources in XAI design, this paper sets out a forward-facing agenda for further attention to the emergent requirements of explainability-in-use.