Designing accessible, explainable AI (XAI) experiences

Explainable Artificial Intelligence (XAI) has taken off in recent years, a field that develops techniques to render complex AI and machine learning (ML) models comprehensible to humans. Despite the growth of XAI techniques, we know little about the challenges of leveraging such explainability capabilities in situated settings of use. In this article, we discuss some particular issues around the intersection between accessibility and XAI. We outline two primary concerns: one, accessibility at the interface; and two, tailoring explanations to individuals' diverse and changing explainability needs. We illustrate these issues by discussing two application areas for AI/Ml systems (aging-in-place and mental health support) and discuss how issues arise at the nexus between explainability and accessibility.