Question-Driven Design Process for Explainable AI User Experiences

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.

arXiv commentworking paper

References in corpus (8)

• A Unified Approach to Interpreting Model Predictions
• Towards A Rigorous Science of Interpretable Machine Learning
• Explaining Explanations in AI
• One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
• Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
• Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
• The Emerging Landscape of Explainable AI Planning and Decision Making

Cited by in corpus (5)

• Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
• Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability
• Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
• Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions
• Better Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI