Designer-User Communication for XAI: An epistemological approach to discuss XAI design

TL;DR

This paper explores how to facilitate effective communication about explainable AI (XAI) between designers, developers, and end-users, emphasizing early-stage discussion and stakeholder involvement using the Signifying Message framework.

Contribution

It introduces an epistemological approach employing the Signifying Message to operationalize XAI discussions among diverse stakeholders in AI system design.

Findings

The Signifying Message framework helps structure XAI scenarios.

Early stakeholder involvement improves XAI understanding.

Application to healthcare AI demonstrates practical utility.

Abstract

Artificial Intelligence is becoming part of any technology we use nowadays. If the AI informs people's decisions, the explanation about AI's outcomes, results, and behavior becomes a necessary capability. However, the discussion of XAI features with various stakeholders is not a trivial task. Most of the available frameworks and methods for XAI focus on data scientists and ML developers as users. Our research is about XAI for end-users of AI systems. We argue that we need to discuss XAI early in the AI-system design process and with all stakeholders. In this work, we aimed at investigating how to operationalize the discussion about XAI scenarios and opportunities among designers and developers of AI and its end-users. We took the Signifying Message as our conceptual tool to structure and discuss XAI scenarios. We experiment with its use for the discussion of a healthcare AI-System.