Question-Driven Design Process for Explainable AI User Experiences

TL;DR

This paper introduces a question-driven design process for creating explainable AI user experiences, aligning user questions with suitable XAI techniques to improve understanding and collaboration.

Contribution

It proposes a novel design process grounded in user questions, bridging the gap between technical XAI methods and UX design, with a practical healthcare use case.

Findings

Mapping guide between user questions and XAI techniques

Enhanced collaboration between designers and AI engineers

Successful application in healthcare adverse event prediction

Abstract

A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques to reframe the technical space of XAI, also serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.