Development of a persuasive User Experience Research (UXR) Point of View for Explainable Artificial Intelligence (XAI)

TL;DR

This paper introduces a practical UX research framework to help UX professionals design accessible and trustworthy XAI interfaces, bridging technical explainability methods and user-centered design.

Contribution

It presents a novel UX Research Playbook for XAI, enabling non-expert UX professionals to create more understandable and trustworthy AI experiences.

Findings

Provides actionable guidance for UX design in XAI

Bridges gap between technical explainability and user-centered design

Empowers UX professionals to improve AI transparency

Abstract

Explainable Artificial Intelligence (XAI) plays a critical role in fostering user trust and understanding in AI-driven systems. However, the design of effective XAI interfaces presents significant challenges, particularly for UX professionals who may lack technical expertise in AI or machine learning. Existing explanation methods, such as SHAP, LIME, and counterfactual explanations, often rely on complex technical language and assumptions that are difficult for non-expert users to interpret. To address these gaps, we propose a UX Research (UXR) Playbook for XAI - a practical framework aimed at supporting UX professionals in designing accessible, transparent, and trustworthy AI experiences. Our playbook offers actionable guidance to help bridge the gap between technical explainability methods and user centred design, empowering designers to create AI interactions that foster better understanding, trust, and responsible AI adoption.